Discovering a new crystalline material has traditionally meant years of trial and error in the lab. Researchers are now treating the search itself as a giant optimization problem, one that a quantum computer is built to solve.

Every crystal’s properties come down to how its atoms are arranged, and finding the lowest energy configuration among countless possibilities is what makes that search so difficult. That arrangement determines whether a material ends up useful in a battery, a solar panel or a catalyst.

“This is a material known as lithium cobalt oxide, and I carry it with me every day. That’s my phone,” said Vladimir Gusev, a lecturer in computer science at the University of Liverpool.

Crystalline materials are defined by long-range atomic order, and lithium cobalt oxide is a textbook example.

Discovery normally follows a loop, according to Gusev’s presentation. A chemistry is selected, a possible structure is predicted, a sample is synthesized in the lab, and its properties are assessed before the results feed back into the next round of predictions.

Gusev said the idea traces back to a study his team published in Nature in 2023, which framed crystal structure prediction (CSP) as an integer programming problem.

Given a chemical composition, the goal is to find the atomic arrangement that minimizes interaction energy.

He said the challenge comes in two parts:

• Calculating that energy from quantum mechanics, through a method called density functional theory (DFT), is computationally expensive; and

• the number of possible atomic arrangements explodes combinatorially as a structure grows.

“Maybe we can use quantum computers to solve these kinds of problems in materials,” he said.

By encoding each possible atom position as a binary variable, the integer program becomes a quadratic expression, known as a quadratic unconstrained binary optimization (QUBO) problem, which is well suited to D-Wave’s quantum annealers. In strontium titanate, for example, the model must also satisfy exclusivity constraints that stop two atoms sharing a site.

Checking every possible arrangement by brute force becomes impractical once a structure grows beyond a handful of atoms. That combination of optimization and quantum hardware is what Gusev’s team has spent the past two years refining, moving from the original 2023 study to the quantum experiments described in his talk.

Learning from known crystals

Gusev presented the research at Qubits Europe, a quantum computing conference held in London. The event was organized by D-Wave and focused on real-world applications of annealing and gate-model quantum computing across industries including materials science.

He works on CSP at the University of Liverpool, applying computational and machine-learning methods to discover new materials.

The original 2023 method relied on classical force fields, treating atomic interactions as a combination of electrostatic attraction and repulsion. Gusev said that approach only works for a limited set of well-studied chemistries, which has limited the method's practical use more broadly.

To fix that, he said a colleague compiled structural data from nearly all previously cataloged crystalline materials and measured how often specific pairs of elements occur at particular distances, building a distance profile for each pair.

A simple mathematical transform then converts that statistical pattern into an energy-like potential.

The result is a data-driven potential that can model interactions between virtually any two elements without requiring bespoke force-field data for each chemistry. That is what lets the quantum annealing approach extend beyond the handful of materials covered in the 2023 study.

Gusev compared the technique to a radial distribution function, a concept familiar from physics. He said the concept describes how likely atoms are to be found at a given distance from one another, and that the team evaluated the new potential the same way it evaluates any other CSP method, rather than using simple test cases.

Running on D-Wave’s Advantage System 4.1, which has 5,621 qubits, the team tested crystal structures of increasing size and complexity. Small, simple structures succeeded at high rates. Titanium dioxide in its anatase form, encoded with just 6 binary variables, achieved a 78% success rate, while strontium titanate achieved 65%.

Success fell sharply as structures grew larger and required more qubits to encode. Rutile titanium dioxide dropped to 15%, aluminum oxide to 3.5%, magnesium aluminate to 0.08% and yttrium oxide to 0.5%.

Three of the largest structures tested, including a calcium aluminosilicate garnet and a cobalt arsenide, returned no successful results.

“Computer scientists like me are all about efficiency. If it cannot run on your laptop, that is a bad algorithm and you need to make a new one. People in chemistry tell me something different. [For them], it is only a problem if it cannot run on a supercomputer in a few days,” Gusev said.

Toward a virtual lab

Not every part of the problem maps neatly onto the hardware. Gusev said the energy function in the team’s model is fully connected, meaning every atomic position variable interacts with every other variable, which does not naturally fit within D-Wave’s native qubit layout.

That mismatch is why D-Wave has been developing hybrid solvers that combine classical and quantum processing, letting the team keep running the same models without redesigning them specifically for the hardware.

“We haven’t optimized it for D-Wave specifically. We’re just taking our modeling and applying it here,” he said.

He described the current implementation as a work in progress rather than a finished product, noting that the team built its models around the science first and is only now adapting them to fit D-Wave’s specific chip.

The stakes extend well beyond any single chemistry. Crystalline materials sit inside lithium-ion batteries for electric vehicles and consumer electronics, solar farms and solar-integrated buildings, and the catalysts and chemicals used to produce green hydrogen, according to Gusev’s presentation.

He also pointed to semiconductors and other quantum technologies as further beneficiaries.

He said the field is moving away from trial and error toward a computational, database-driven pipeline, one built around machine learning methods his group described in a 2021 study in Nature Communications, used to select which chemistry to explore next.

From there, the pipeline predicts the structure that the chemistry forms and experimentally tests the most promising candidates.

He said better catalysts and materials also support resilience against oil supply shocks by strengthening the technologies that reduce dependence on oil, from batteries and solar power to green hydrogen production.

He added that much of modern manufacturing still relies on catalysts that researchers in crystalline materials aim to improve.

Gusev sketched where the work could lead, combining the optimization framing of CSP, the data-driven potential built from known materials, and D-Wave’s quantum annealer into a single discovery pipeline that also draws on machine learning.

He said all three elements feed into the same goal of materials discovery. The goal is to treat the quantum annealer as a reusable virtual chemistry lab, one capable of taking in any chemistry, proposing candidate structures for almost any composition and pointing researchers toward which materials are worth attempting to synthesize next in the real laboratory.

A quantum computing company has become the first to build both major types of quantum hardware.

The milestone follows a recent acquisition that brought advanced gate-model hardware in-house, adding to an existing lineup of annealing-based systems used for optimization work across industries ranging from manufacturing to logistics.

“D-Wave is the only dual-platform quantum computing company in the world,” said Andrei Petrenko, vice president of gate product management at D-Wave Quantum. “That means we are the only one who can provide products and solutions that target all classes of problems.”

No other publicly traded quantum computing company currently sells commercial hardware in both categories, annealing and gate-model, giving it a broader product line than its rivals, according to the company.

“These qubits are simply high-performance hardware,” Petrenko said. “They have world-class fidelities, and in the same package they’re high-speed devices.”

Its own benchmarks put single-qubit gate fidelity at 99.99%, two-qubit gate fidelity at 99.85% and measurement quality at 99.97%. The hardware combines the speed and ease of control of superconducting circuits with the high performance of natural qubits such as ions and atoms, which are typically too slow for practical use.

Quantum Circuits Inc, the company D-Wave acquired to gain the technology, was co-founded by physicist Rob Schoelkopf, a pioneer of the dual-rail qubit approach who is now its chief scientist.

The three-year roadmap runs from 2026 through 2028 across three processors called the DR17, the DR49 and the DR181, with the numbers reflecting each chip’s dual-rail qubit count. The DR49 is due in 2027 with a scalable module design and real-time control flow, ahead of the DR181 the following year.

“The two central goals are error correction and near-term use case exploration, and the idea is from the DR17 through the DR181 to demonstrate a blueprint for fault-tolerant quantum computing,” he said.

The DR181 aims for a logical qubit error rate 2,000 times lower than the DR17’s physical qubit rate by 2028.

“We have high qubit performance and high speeds, plus consistent performance across our devices, something competitors don’t like to talk about,” he said.

D-Wave Quantum, listed on the New York Stock Exchange, has sold commercial annealing-based quantum computers for years and is now expanding into gate-model computing, after acquiring Quantum Circuits Inc in January 2026 in a deal reported to be worth roughly $550 million, among the largest in the young quantum computing industry.

Error detection built in

Petrenko presented at Qubits Europe, a quantum computing conference D-Wave organized in London in June, focused on real-world applications of quantum computing across industries. He previously served as head of product at Quantum Circuits Inc before the acquisition.

“No other conventional qubit modality can do this,” he said. “You can’t do this in a trapped-ion system, you can’t do this with neutral atom qubits, and you can’t do this with the typical transmon qubits that IBM, Google or anyone else uses.”

Photon loss is the dominant error mechanism in a dual-rail qubit and can be detected with high accuracy, he added.

The company calls this capability “mid-circuit error detection” (MCED), giving users a third data point beyond the usual zero or one. Paired with a separate error-aware measurement tool, it flags whether a qubit’s result can be trusted without disrupting the calculation.

“The mid-circuit error detection can be placed wherever you want in the algorithm, and it returns a result, a zero or a one, without disrupting the state of the qubit,” he said. “For 181 qubits, you achieve an error rate better than 10^-6.”

A conventional logical qubit needs roughly 1,000 physical qubits to reach that same rate, while its dual-rail approach needs roughly 100, a tenfold saving in hardware. It expects the hardware to aid pharmaceutical and materials research within one to three years, and AI applications further out.

Mayowa Ayodele, D-Wave’s lead solutions architect, also presented at the conference. She holds a doctorate in operations research and works with global enterprises on scheduling, logistics and finance problems.

The optimization team, meanwhile, spends much of its time helping customers determine whether their problems are genuinely suited to quantum computing, weighing complexity, scale, and the cost of building a solution against the value it might deliver, before any code is written.

“The keys to success fall under three main areas,” Ayodele said. “The first is use case selection, the second is the right formulation, and the third is customer engagement.”

She pointed to its drug discovery work and a demonstration that replaced nearly a million years of computation with minutes.

“When it comes to problems we expect quantum to be better at, we are talking about three things: problems that are complex and large and have discrete decisions,” she said.

A pharmaceutical manufacturing example illustrates the point. Two production processes share early steps such as dispensing, blending and drying before diverging into either compression and coating or milling and encapsulation, with shared resources creating bottlenecks and scheduling constraints across both lines.

Discreteness comes in two forms: choices that are naturally discrete and continuous values that can be sensibly rounded.

“If I put this much money into creating that solution, am I going to get a higher return? Is it worth it?” she said.

That thinking underpins D-Wave’s Stride solver, which blends quantum annealing, tensor programming and other optimization methods.

Proven results in production

Once a problem clears that bar, D-Wave walks customers through a five-stage process before any solution reaches production, with each stage having its own checkpoint.

The stages run from Discovery through Proof of Technology (POT), Proof of Concept (POC), Pilot and Production, separated by three validation gates. Each stage answers a different question, from “Should we do this?” at the start to “Does it scale and deliver ongoing value?” at the end.

“As part of the contract, we offer customers the opportunity to take a training course, so they understand the context,” Ayodele said.

Its professional services team understands each solver’s strengths and how to match them to a customer’s rules.

“Our clients are the experts,” she said. “They know the problem more than we ever could, and they know what success looks like within their own business.”

Ford Otosan, a joint venture between Ford Motor Company and Koç Holding in Turkey, built a hybrid quantum application to optimize vehicle production sequencing in its body shop, aiming to cut downtime and support real-time decisions as parts and schedules change on the factory floor.

“It took them 30 minutes to schedule 1,000 vehicles,” Ayodele said. “With our solution, they could do that in less than five minutes.”

The solution also gave Ford Otosan more flexibility to adapt to changes in demand or parts availability.

A similar approach helped BASF, the world's largest chemical producer, which faced a scheduling challenge across its chemical production operations.

“It took another solver several hours,” Ayodele said. “With our Stride solver, it’s about five seconds, which we thought was really massive.”

D-Wave said the approach cut product lateness by 14%, reduced setup times by 9% and shortened tank-unloading time by 18%.

Its own roadmap for the optimization business points toward 100,000-qubit annealing systems within five years, alongside the newer gate-model line, which is still working toward fault tolerance.

D-Wave is now pursuing that same pattern of incremental wins on two fronts at once, refining its annealing systems for today’s optimization customers while pushing its new gate-model hardware toward fault-tolerant computing by the end of the decade, the two platforms advancing in step.

A quantum algorithm has cut cardboard use by more than 200 kilograms across a benchmark of real orders.

The comparison covered 6,034 real orders, checked against the classical solver already running in the company’s production line and against what the existing software would have produced for the same shipment. Every box and kilogram of cardboard avoided lowers shipping costs and cuts material waste.

The same algorithm is projected to save the manufacturer €480 a day. It packs thousands of orders every day.

“They need to face thousands of bin packing problems every day. When I say thousands, I mean 6,000 of these problems every day,” said Dr. Eneko Osaba, principal researcher at Tecnalia. “The algorithm we have developed is better because it uses fewer boxes, with cardboard use more than 200 kilograms less.”

“We can say our algorithm is more sustainable. If they decide to deploy it, they are going to save €480 each day, which I think would be a very good indicator of whether you want to use a solution or not,” he said.

He said companies want to talk about money and sustainability, since those are the indicators they focus on.

Osaba works in Tecnalia’s quantum technologies group, a Spanish center that calls itself the country’s largest.

The client behind the benchmark, Würth, is a global market leader in the development, production and sale of assembly and fastening materials. Tecnalia describes its own mission as turning technology into gross domestic product, treating quantum computing as an applied research area it sells directly to industry rather than a purely academic exercise.

Osaba said the benchmark tracked several business indicators together rather than a single optimization score, including the total number of boxes used, the amount of cardboard consumed and the resulting shipping cost.

The team frames that combination as its clearest business case yet for quantum computing: fewer boxes, less material, lower cost.

The Würth case is the most advanced among several industrial quantum computing projects that Tecnalia has built with external partners. Osaba said each of the group’s published use cases has come with its own peer-reviewed paper, and this one is next in line.

Why boxes are hard

Osaba presented the work in a session titled “Applied Quantum Computing: 3D Bin Packing for Würth” at Qubits Europe 2026, a one-day quantum computing conference organized by D-Wave in London. The event focused on real-world business applications of quantum computing.

He said the bin packing problem (BPP), a decades-old logistics challenge, remains far from solved even with today’s technology. The task is to fit items of different shapes into containers using as few boxes as possible, a problem that gets harder as more items are added.

“It is very important even today, and it is not already solved,” he said. “The three-dimensional bin packing problem is complex because it is discrete, which makes it very interesting for quantum computers. It has a huge number of possible combinations.”

A real-world version adds further constraints: each bin has a maximum weight it can support, and every item must fit inside without extending beyond it. Würth’s operation packs items such as screws, nuts and latex gloves into three standard box sizes, with liquids that cannot be rotated among its trickiest constraints.

To solve it, Osaba’s team built an iterative process blending classical computing with quantum hardware.

“Our approach is an iterative algorithm,” he said. “First we do pre-processing, and based on the volume of all the items we need to package, a classical algorithm makes a first prediction. If all the items fit into the boxes it predicts, we have a solution.”

“We build the CQM [constrained quadratic model] formulation with D-Wave’s Leap CQM hybrid solver,” he said.

If the first prediction proves infeasible, the algorithm relaxes the configuration, for example switching from two small boxes to one small and one medium box, and repeats up to ten times before it stops.

During the session, Osaba ran a live query to show the process end-to-end. He picked one real order from a library of more than 6,000 stored instances, selected the Tecnalia solver built on D-Wave’s technology, and let the system return a packing layout in front of the audience.

Each run produces a results file that includes a visual layout of the final box, along with the cardboard and cost figures. In one example order Osaba showed, 17 separate packages were fitted into a single small box.

Proven on real hardware

The results were not produced by simulation. Osaba said the benchmark ran on real quantum hardware.

“This is not an emulation. This is not a simulation. This is not quantum inspired. This is real quantum computing,” he said. “We are not working with a synthetic data set. We are solving more than 6,000 real orders, and the executions ran on real hardware.”

“We have validated every result against the real production workflow,” he said.

The benchmark was run on D-Wave’s Advantage 2 quantum annealer via QCentroid, a Bilbao-based platform that has partnered with Tecnalia since 2024. He selected each order from a library of more than 6,000 stored instances before submitting the job.

“Tecnalia is the largest applied research and technology vendor in Spain. We are around 1,500 people working across more than 14 technologies,” he said. “Within Tecnalia, there is a smaller area called data transformation, with around 120 people. Within that group is quantum technologies, where I work; we have at least 27 people.”

“We act as a middleware between companies and universities. We have academic projects at a low technology readiness level, and more mature projects with companies solving real-world use cases,” he said.

He said project conceptualization for the Würth case came from Telefónica. Tecnalia’s team will continue testing its algorithms against other real-world logistics and manufacturing problems.

A Taiwanese manufacturing giant best known for assembling iPhones is closing in on its first working quantum computer.

A prototype is expected within the year, capping a five-year effort that began with theoretical study before hardware work started three years ago, when researchers chose ion traps over the more common superconducting qubit approach.

“Foxconn started this about five years ago. In the initial two years, they only did theoretical study and recruited people from around the world, but three years ago, they started building hardware,” Ching-Ray Chang, board director of Hon Hai Technology Group (Foxconn) and a professor at the Chung Yuan Christian University, told TechJournal.uk in an interview in London.

“Probably next year they will try to at least deliver the prototype,” he said. “Foxconn still needs to catch up. They are at a very initial stage, but this can operate.”

Foxconn’s Hon Hai Research Institute set up an Ion Trap Quantum Computing Laboratory, or IonLab, in late 2023, which lab director Guin-Dar Lin has called Taiwan’s first fully corporate-funded quantum research and development base.

The lab has laid out the program in three phases.

• Short term, by 2027: build a universal ion-trap prototype with 5 to 10 qubits, meaning general-purpose hardware like a classical computer, programmable for many tasks. Too few qubits to solve practical problems.

• Medium term: develop the technologies needed to scale up, moving from today’s 3D ion traps to 2D semiconductor-fabricated chips, plus ion-shuttling and integrated optical and electronic controls.

• Long term: build stable, modular architectures supporting hundreds, thousands or even tens of thousands of qubits. Once mature, the lab will pursue fault-tolerant algorithms aimed at powering advances in AI, energy storage and pharmaceuticals.

Asked whether tightening US export controls could slow Taiwan’s quantum ambitions, Chang drew a direct comparison with the island’s semiconductor history.

“Fifty years ago, when Taiwan started building its own semiconductor industry, nothing was classified. Everyone shared knowledge with each other, though of course you had to pay for it,” he said. “Right now, even if you want to pay, sometimes you still cannot get the transfer. It’s a different situation.”

He said the shift is pushing countries toward self-sufficiency rather than collaboration.

“Every country is trying to build its own, because this is some kind of sovereignty issue,” Chang said. “They have probably learned a lot from the semiconductor industry. You need to control things yourself. You cannot rely on others.”

“Not only the patents, but also the production,” he said, when asked whether the concern extended beyond patents.

From years to days

Chang set out the case for quantum-assisted materials design in a keynote at the Commercialising Quantum Global 2026, an event organized by Economist Enterprise in London.

He framed the shift as a generational handover, pointing to a 60-year cycle that took computing from the 1940s vacuum tube through the 1970s integrated circuit to 2000s supercomputers, and predicting an equivalent buildout for quantum computing through 2086 and beyond.

“Materials shape the world,” Chang told the conference. “Before the 20th century we used trial and error, over years, to identify compounds. In the next 30 years we will probably use quantum and artificial intelligence (AI) combined to identify new materials.”

He said the limits of that trial-and-error approach still apply today, even with AI involved.

“Right now we use trial-and-error, whether by humans or by AI, and it takes far longer than we would like,” he said. “That’s why we have never been able to design good materials in time.”

Chang pointed to a project with Formosa Plastics Corporation, Fujitsu and his own university.

The team screened phenol-based inhibitors for lithium-ion battery additives, targeting compounds with the lowest bond dissociation energy, using quantum-inspired digital annealing rather than a full quantum computer.

Defining the problem took up to two years before the algorithm could be applied to the roughly 50 candidate compounds.

“If you calculate this the conventional, classical way, each combination takes about two or three hours, and finishing all the combinations could take six years,” Chang said. “After we used the algorithm to write the energy formula, we could gather the five lowest combinations almost instantly.”

Published results from the project put the acceleration at a more modest tenfold, cutting screening time from weeks to days, with accuracy comparable to high-precision quantum-chemistry calculations.

He pointed to examples from beyond his own team.

A collaboration involving IBM, RIKEN and the Cleveland Clinic used classical AI for protein analysis, a high-performance computer to fragment large molecules, and up to 94 qubits to calculate exact electronic wave functions, handling more than 6,000 quantum operations in the process.

The molecules simulated grew from a 10-atom methane dimer to a 12,635-atom protein in less than two years.

Separate groups have used similar analog approaches:

• Silicon Quantum Computing (SQC) simulated a metal-insulator transition with more than 15,000 silicon quantum dots in February.

• The University of Science and Technology of China (USTC) modeled an antiferromagnetic phase transition with 800,000 cold atoms.

• The French firm Pasqal simulated a similar frustrated-magnet system with 256 cold atoms in March.

• Quantinuum used 72 qubits and 18 additional ancilla qubits in a gate-based experiment published in February to observe pairing correlations linked to superconductivity, though without yet demonstrating a working current.

Chang said a more robust quantum computer would be needed to take the next step, potentially identifying a room-temperature superconductor.

Chang said rivals are moving faster on raw qubit counts than Foxconn.

“IonQ says that by 2030 they can have more than 80,000 logical qubits,” he said. “If that is true, I think the whole world will change.”

“In the future, this will definitely be the best way to design materials,” he said. “Use AI to screen down a large sample to a smaller set, use the quantum way to solve it exactly, then transfer the result to AI and robotics to run the automatic synthesis pipeline.”

Chang listed the payoff on offer: room-temperature superconductors, programmable RNA biomaterials, rare-earth-free magnets, solid-state batteries, fusion reactor materials, and catalysts for green chemistry, artificial photosynthesis and next-generation semiconductors.

Chang illustrated the stakes with a comment from Mitsunobu Koshiba, former president and chairman of JSR Corporation and now an external director of Rapidus Corporation, who was speaking at the Q2B quantum technology conference in Tokyo on June 4.

Koshiba estimated that labor generates about 2% profit, capital about 8%, and digital technology as much as 40%. He said quantum computing could produce outsized returns, since computational power scales nonlinearly with hardware gains.

Racing on three fronts

Taiwan’s quantum push is running alongside its semiconductor strength, though the island’s small population is starting to show.

“Taiwan’s semiconductor industry is quite strong and we benefit a lot from that. Recently, AI has made Taiwan’s GDP grow very fast, but Taiwan doesn’t have a large population, so human resources are not enough right now,” Chang said.

“We developed quantum technology late, but in the last three years the research and development side really caught up quite fast,” Chang said. “In Taiwan, we are focusing on three tracks: superconducting qubits, trapped ions, which Foxconn is focused on, and photonic.”

He said Foxconn’s own interest in ion traps is tied to a specific commercial goal beyond research prestige. The company is trying to build an EV battery that is as safe and long-lasting as possible. Trial-and-error is not easy for finding the best material, so in-silico design is much better with the help of a quantum computer.

Foxconn has already published two projects toward that goal.

• With QunaSys, it released a neural-network-assisted compression framework in October 2025 that uses one of Foxconn’s own patents to cut the hardware resources needed for battery chemistry simulations.

• With Quobly, it released an open-source toolbox in May 2026 for quantum phase estimation.

The technique is aimed at running fault-tolerant simulations on classical computing grids using tensor networks, giving Foxconn a head start before its own ion-trap machine is ready.

Chang’s own standing in the field was recognized in December 2025, when he was named an honoree in the UN-backed Quantum 100 initiative. He has authored more than 280 academic papers and holds more than 28 patents.

He previously served as executive vice president and acting president of National Taiwan University, where he founded the NTU-IBM Quantum Hub and introduced quantum computing into its formal curriculum. He also set up the Quantum Information Center at Chung Yuan Christian University.

Europe has repeatedly opened the door to a lead in quantum computing, only to watch the advantage slip away.

Former European Central Bank chief Mario Draghi warned in 2024 that the region’s technology gap with the United States and China was widening. Britain, meanwhile, remains excluded from parts of the European High Performance Computing infrastructure.

The cause is not a shortage of money, according to one health-tech entrepreneur at the center of the field. It is Europe’s own instincts.

A habit of leaving markets to sort themselves out, paired with 27 member states each rooting for their own national champions, has slowed the kind of coordinated push that built rivals’ early leads.

“There’s some kind of European DNA that perhaps needs to be adapted for us to join the leadership in quantum,” said Sella Brosh, chief executive of NVision. “The member states each pull in different directions, rooting for their own champions, and that has always been the case.”

“Europe needs to set the standards on its own and have the same level of centralized expertise that the US government has built,” he said.

Brosh founded NVision in Ulm, Germany, in 2015.

Its POLARIS platform boosts magnetic resonance imaging (MRI) signals to measure metabolism in real time, letting doctors judge how a tumor is responding to treatment within days rather than the months traditional scans can take.

POLARIS is already installed at close to 20 cancer centers worldwide, including Memorial Sloan Kettering Cancer Center, the University of Cambridge and the Technical University of Munich. NVision raised $55 million in a Series B round anchored by healthcare giant Abbott in May 2026, bringing its total funding to $120 million.

Abbott joined as the round’s sole strategic investor in diagnostics, and NVision was also named a winner in Fast Company’s 2026 World Changing Ideas Awards this year.

The new capital backs a computing platform called Photonic Integrated Quantum Circuits (PIQC), using organic-molecule qubits on photonic chips to speed drug design.

NVision announced a further expansion in July, into South Korea, where a €2.5 million Horizon Europe-backed project will deploy POLARIS at Yonsei University to study brain metastases, one of oncology’s hardest imaging challenges.

Europe has deep scientific knowledge across architectures, error correction and qubit types, Brosh said, but researchers have stayed too long in an exploratory phase without pulling it together to scale.

“I think the good news is it’s not too late,” he said. “We can still bring that into Europe.”

He said that message is one of the most important he wanted people to take away. He said that once standards are set, architectures can be stress-tested and funding directed at what works, letting Europe own the chokepoints in the technology.

He warned that without that internal expertise, the proprietary knowledge needed to compete simply will not exist on the continent.

Whoever wins compute

Brosh made the comments in a fireside chat with Jason Palmer, host of The Economist’s “The Intelligence” podcast, at the Commercialising Quantum Global 2026 conference in London. The event, organized by Economist Enterprise, examined how Europe can secure sovereign capability in quantum hardware, supply chains and talent.

Quantum is not an ordinary industry, Brosh said. It is a general-purpose technology that reshapes everything it touches.

“Quantum will be the best of times and the worst of times for those who do not own it,” he said. “Whoever wins compute wins everything.”

He said he discussed the idea with NVision investor Pat Gelsinger, the former chief executive of Intel. Better materials, better chemistry and optimized logistics all flow from compute, he said, calling it an enabling technology for nearly every other industry.

Brosh pointed to two recent developments as warning signs set against that promise.

• The United States tightened control over advanced artificial intelligence (AI) models such as Claude Fable 5, briefly restricting exports after its June 2026 release before lifting the curbs weeks later.

• Google published a study cutting by 84% the qubits needed to break the cryptography underpinning Bitcoin, using a zero-knowledge proof that withheld the method.

“Shor’s algorithm, the cybersecurity issues and the ability to use machine learning to manage overwhelming amounts of data for mass surveillance represent the worst of times,” Brosh said. “I don’t think Europe wants to ask permission from anyone to use quantum.”

He pointed to the United States’ Quantum Benchmarking Initiative (QBI), run by the Defense Advanced Research Projects Agency (DARPA), as a concrete model for Europe to copy in building its own domain expertise. A company is never fully eliminated from the program even if it hits a bottleneck in the assessment process. Participants can return once they have a credible path to scale.

“What was special about this is that it created a continuous intellectual conversation between companies and DARPA,” he said.

He said the program draws on around 500 experts assessing paths to scale across different qubit types, including photonics.

DARPA’s own published structure confirms a three-stage evaluation running through 2033, with companies advancing through architecture review, detailed research planning and independent hardware verification. Eleven companies, including IonQ and Quantinuum, have already advanced to the program’s second stage.

“That intellectual conversation between the government and the companies is one of the strongest driving forces behind the dominance of the US,” he said.

Owning the chokepoints

Europe had the initiative once. Brosh said the region was the first mover in quantum computing, ahead of both China and the US, thanks to the European Union’s Quantum Manifesto, launched in the Netherlands in 2016. It became the bloc’s Quantum Flagship program two years later, in 2018.

Around 2018 and 2019, NVision received more funding from the European Commission than from private investors, Brosh said. That public backing let the company grow before it eventually attracted more private capital.

“We kept continuing this exploration phase without starting to consolidate and understand what could actually scale into fault-tolerant quantum computers,” he said. “Nobody knew at that stage.”

Brosh said Europe can still close the gap.

“We still have time. There are so many chokeholds in building these scalable systems that Europe can still find the places where it controls them or is part of the critical value chain,” he said.

He cited IMEC, the Interuniversity Microelectronics Centre in Belgium, as one of Europe’s strongest assets in silicon photonics.

He also pointed to deep quantum-algorithm talent in the UK. All 27 EU member states have already signed the European Declaration on Quantum Technologies (EDQT), committing to build what the bloc calls a “quantum valley” of research and industry, a framework Palmer referenced during the discussion.

Brosh said Europe should not simply copy the US and DARPA’s approach. It should build its own version, starting with hands-on domain knowledge.

“It starts with knowledge: building a program that is equivalent to the QBI,” he said. “We need to understand what is really needed, from first principles, to build a fault-tolerant quantum computer.”

He said the work needs to start now, before the chokepoints he describes close for good.

Quantum computing’s central fight is not over restriction, but over who qualifies as a trusted partner.

Governments broadly agree that some of the technology must stay guarded, but there is far less consensus on how to categorize the allies, partners and potential rivals who should decide who gets access to what, and on what terms of trust.

“I see us as an accelerator, not the end point or the primary end user for this,” said Chester Butterworth, head of strategy, disruptive capabilities and technologies at the Royal Navy of the United Kingdom.

He said defense can act as an early risk taker, working with small businesses, academics and laboratories long before industry is ready to scale a technology, contrasting the handful of systems a warship might need against the thousands required for 6G infrastructure.

“If we select some half of the world and give access to that, that is a real, serious break to global stability,” said Jorgen Ellegaard Andersen, professor at the University of Southern Denmark and chief executive of Qpurpose.

“In terms of assured capability, who are our allies and partners? How do we work with NATO to create a stronger alliance, where we are able to interoperate, interchange our technology and take the best of what is available at large?” Butterworth said.

He said this collaborative approach, rather than isolation, reflects what he called a renewed momentum for quantum in Britain.

“As much as we want to aspire to open ecosystems, we’re sadly not in an open ecosystem world right now,” said Romi Sumaria, chief commercial officer of QAI Ventures.

Oscar Diez, head of quantum computing at the European Commission, said cooperation must rest on reciprocity, not open-ended access.

Money follows the moment

The debate took place at the Commercialising Quantum Global 2026, organized by Economist Enterprise, in London. Cailin Birch, principal global economist at Economist Intelligence, moderated the panel on whether quantum computing will fragment into competing blocs or grow into an open ecosystem.

The panel brought together defense, academic, government and venture capital perspectives on an industry moving toward commercial deployment.

“Let’s just look at quantum hardware. How many IPOs [initial public offerings] of the hardware are happening outside the US? That is the problem we have at hand,” Sumaria said.

Andersen said pension funds favor safe bonds over risky, early-stage technology, warning that Europe cannot afford to repeat past mistakes.

“We have basically lost on chip production. Personal computers and AI, you name it. If we lose it on quantum too, we are not doing really well, so we have to be there,” he said.

Diez pointed to Pasqal and IQM, two European-founded quantum computing firms that have listed, or are preparing to list, on the New York Stock Exchange instead of a European one, calling it part of a wider European problem in scaling technology companies to late-stage growth.

“When you go into the final rounds, it is very difficult to find funding here in Europe,” he said.

“America is driving capital flows into the hardware and infrastructure segment. Europe is driving innovation and fostering collaboration, but we have nowhere near the scale of capital needed,” Sumaria said.

Britain, though, is trying to change that calculus.

Butterworth pointed to what he called Britain’s “quantum moment,” a £2 billion government quantum package unveiled in March 2026, made up of £1 billion to procure large-scale quantum computers and a further £1 billion over four years to help the industry scale.

He said the funding follows new competitions to build quantum supply chains, with money set aside for that purpose.

“What I see internally from government is a significant increase in collaboration, not just between defense and security, but how defense talks to the Department for Transport, the Department for Business and the Office for Investment,” he said.

He said even the best quantum sensors are of little use without a supply chain capable of producing them at the scale an industry like aviation would need; the government recognizes the gap and is working to close it.

He added that the Royal Navy applies government-funded exchange programs to test technology at sea.

Software still lags hardware

The panel’s sharpest disagreement was not about money, but about what to build first.

Andersen cited a recent talk from Rolls-Royce, where it took five years to get a finished quantum algorithm into operational production. He said the industry is already behind on software as a result.

“A good example is GPUs. In high-performance computing (HPC) systems, there was a dead period for a long time. The number of GPUs was growing but they were not being used. Eventually people started to learn to use them, and now there is a big hype about that, which is AI,” he said.

“It is going to be the other industries, not the quantum industry, that pull this in the long run. Industry has to pull, and it pulls when it sees useful use cases,” he said.

He said that alone will not close the gap.

“We are having this European initiative where we benchmark all existing software and make it totally open source, so everybody can see what the good and bad ones are,” Andersen said.

One such initiative is Open QBench, an open-source EuroHPC JU benchmarking framework developed by EuroQCS-Poland.

Diez said the picture is more complicated than a simple hardware and software split. The European Commission has set up six pilot production lines and a software center of excellence, and has connected quantum computers to HPC centers for four years, expanding from six machines to eight, with two more planned.

“We have a similar number of companies at European level in quantum, very similar to the number in the US. I think it’s relatively healthy,” he said.

The gap has consequences for talent as much as technology.

“The defense budget is catastrophically higher in the US than anywhere else, and that is the first industry to adopt these technologies. Historically, defense innovates and then industries adopt from there,” Sumaria said.

Singapore is trying something similar through Enterprise Singapore, encouraging researchers to build local ventures.

“It gives an unfair advantage on talent acquisition. If you are a scientist and we give you an amazing test bed with all the best facilities, it is like a Disneyland for scientists,” he said.

He said the clock is already running.

Sumaria said Europe needs to double down on the technology now, warning that Asia is catching up fast in areas where Europe has previously led.

His biggest frustration, though, was with the frame of the conversation itself.

“We focus mainly on Europe, the US and the UK. What about China? What about Singapore? What about the Middle East? China is spending billions every year, and we do not really have a view of what is being built there,” he said.

“Look at India. It is the same concept. They are investing heavily in quantum, but they have shifted their policy from a software era, where they exported talent, to building infrastructure in-house,” he said.

Sumaria said his own firm’s strategy reflects that same shift toward specific sectors, including life sciences beyond drug discovery into diagnostic medicine, financial services built around quantum cryptography and industrial applications where deep subject matter expertise meets a well-defined problem, the kind of pairing he said drives early adoption.

“Especially at the intersection of quantum photonics, we have a fantastic photonics industry in Europe,” he said.

Butterworth said the UK now has a genuine opening to convert its funding into lasting advantage. Whether Europe can close the software gap, and whether talent stays instead of chasing larger American budgets, will likely determine which vision of the industry prevails.

Blockchain earned its reputation as an unbreakable ledger, immune to tampering. Quantum computing is quietly turning that guarantee into a liability.

The technology that makes blockchain trustworthy relies on cryptographic keys that are extremely hard to crack with today’s computers. Quantum computing, once a distant academic pursuit, is now advancing at a striking pace, and a sufficiently powerful machine could eventually break those keys in a fraction of the time.

That would unravel the very immutability the technology was built to guarantee.

“What was considered to be the best value proposition, I recognized, was at the same time its biggest vulnerability,” said Dino Cataldo Dell’Accio, deputy chief executive of the United Nations Joint Staff Pension Fund (UNJSPF). “That could expose us to a loss of trust and undermine the construction of the model we had put in place.”

Dell’Accio, who has spent most of his 25 years at the UN in information-systems and technology auditing, said he arrived at that conclusion gradually, drilling down from a general unease about emerging technology to the specific mechanics of how quantum algorithms undermine cryptography.

He said he reports the risk every year to the fund’s governing body, made up of representatives from 193 countries, as part of its budgeting process.

The urgency is not unique to the pension fund. The US National Institute of Standards and Technology (NIST) finalized its first quantum-resistant encryption standards in August 2024, known as FIPS 203, 204 and 205, intended to replace RSA and elliptic-curve cryptography across government and industry systems.

The US National Security Agency has set 2030 as its deadline for national security systems to complete the migration.

The UNJSPF was established in 1949 by the UN General Assembly. It manages more than $114 billion in assets for beneficiaries across 192 countries, making it the world’s largest international pension fund.

A threat already here

Dell’Accio discussed the fund’s quantum strategy at the Commercialising Quantum Global 2026 in London. The event, organized by Economist Enterprise, featured a fireside chat moderated by Christina Yan Zhang, chief executive of The Metaverse Institute, centered on transitioning a live, mission-critical identity system to post-quantum security.

Dell’Accio said most public attention on emerging technology has gone to artificial intelligence (AI) rather than quantum computing, which remains largely confined to laboratories. He said working quantum decryption is not purely theoretical, pointing to early laboratory-scale demonstrations that have not yet reached mass adoption. He said that gap is exactly the problem.

The tactic he described is known in the security industry as “harvest now, decrypt later,” a pattern documented by government cybersecurity agencies including the US Department of Homeland Security and the UK’s National Cyber Security Centre.

The European Union Agency for Cybersecurity has issued similar warnings, noting that adversaries are already collecting encrypted data today. Expert surveys put the odds of a capable quantum machine existing by 2035 as high as 50%.

“Adversaries need only patience, not a working machine today,” he said. “I had to convince, persuade and demonstrate: the threat is today.”

“There is a real possibility that ill-intentioned actors are storing offline the transactions we have on our blockchain, waiting for the day quantum computing will enable them to break that encryption.”

Retroactively decrypting that harvested data would undermine a system built specifically to support decentralized digital identity, he added.

The fund’s digital identity platform runs on Hyperledger Indy, an open-source blockchain built for decentralized identity by the Linux Foundation. It also underpins the Sovrin Foundation’s public identity network, letting individuals hold and control their own credentials.

Dell’Accio said his team cataloged every element recorded on the ledger: the root hash, transaction type, timestamp, and sender’s public signature. He said a compromised timestamp is a smaller risk on its own, but it could still quietly undermine the chronological record of transactions.

Grover’s algorithm poses a significant threat to the hashing mechanism, he said.

“The public signature is the element most vulnerable to post-quantum computing, allowing someone to derive the private key and potentially start altering the integrity, availability and trustworthiness of the ledger itself,” he said.

Turning threat into readiness

Dell’Accio outlined the steps now underway at the pension fund to prepare for the shift:

• Identify what data is written to the blockchain and how long it must remain valid

• Take inventory of the cryptographic primitives and mechanisms in use

• Prioritize critical systems and evaluate technical debt

• Assess exposure through third parties

• Migrate toward a post-quantum resistant blockchain platform

“No one system or application lives in isolation,” he said.

The likely destination is a platform built on lattice-based algorithms such as ML-KEM, used for key exchange, and ML-DSA, used for digital signatures. Both are NIST-approved standards now emerging as leading candidates for this kind of migration across the industry.

The fund is also building flexibility to run classical and quantum-resistant computing side by side, he added.

The urgency is rooted in what is already at stake. Dell’Accio said UNJSPF’s blockchain-based proof-of-life system has been running in live production since 2021, verifying roughly 89,000 beneficiaries across more than 192 countries and replacing a paper process that dated back 70 years.

“We are the first and only, as of today, UN entity to achieve ISO [International Organization for Standardization] certification on the responsible use of artificial intelligence (AI),” he said.

The certification, ISO/IEC 42001:2023, covers the fund’s proof-of-life digital identity solution, known internally as the Digital Certificate of Entitlement.

The system also uses biometrics, facial recognition and geolocation to detect deepfakes, and is independently audited every year against international standards.

The British Blockchain Association recognized the solution in 2025 at its international scientific conference.

Zhang, who has collaborated with Dell’Accio through the UN Global Initiative on AI and Virtual Worlds, known as Discovering the Citiverse, recalled that he once replied to one of her proposals within 24 hours with a complete framework spanning 10 areas and 50 indicators.

The initiative is developing global standards on AI readiness for cities worldwide, and Dell’Accio has contributed executive briefings and a pre-implementation checklist to its work. The UN Secretary-General recognized that record with an Innovation Award in 2022.

Other public and private pension funds are now lining up to adopt the same approach, she said.

For Dell’Accio, the next test is migrating a live, high-stakes ledger to post-quantum security without breaking the trust it took years to build.

Most large companies still have no roadmap for quantum computing, even though the window to build a competitive edge is already open, according to Deloitte.

An estimated 80% to 90% of Fortune 500 companies still lack a formal plan to prepare for the technology.

Not as many companies have a roadmap in place as they should, said Scott Buchholz, global quantum computing lead at Deloitte.

"I would guess 80% to 90% may not have one. Arguably not as many as should," he said. "Failing to plan is planning to fail.”

Buchholz likened a quantum roadmap to a call option during the session: a plan companies can leave on the shelf until certain triggers are reached. Engagement varies widely even among the minority of companies that already have one, he said, from cursory contingency planning to substantial ongoing investment.

Inbound interest has grown steadily over the past year, Buchholz said, with requests increasing as national governments invest heavily in quantum research and public attention filters into corporate boardrooms.

Much of that curiosity comes down to fear of missing out, Buchholz said.

Deloitte, the world’s largest professional services network, has spent nearly six years building out its quantum computing practice under Buchholz, who also serves as chief technology officer for its Government and Public Services practice.

The practice advises governments and large companies on quantum strategy and technology adoption.

The firm also publishes an annual Tech Trends report tracking where emerging technologies are headed over the next 18 to 24 months.

A no-regrets bet

Buchholz discussed the strategy in a fireside chat at the Commercialising Quantum Global 2026, organized by Economist Enterprise, in London. The session, on building a “no-regrets” path to enterprise quantum computing, was moderated by Tamzin Booth, editorial director of Economist Enterprise and a former business editor at The Economist.

Booth has covered global business and technology for The Economist for more than two decades, including a stint as the publication’s Tokyo bureau chief.

Buchholz and Booth had discussed quantum-inspired techniques on stage together a year earlier.

Buchholz has worked in quantum computing for almost six years, moving through cycles of optimism and pessimism as the field has developed.

“Of late there’s a lot more optimism than not,” he said, pointing to progress on hardware and algorithms over the past year.

While Quantum computers are not yet able to perform tasks that are genuinely useful on their own, quantum-inspired techniques let companies extract business value today using existing hardware and governance processes, while building the internal expertise needed once true quantum systems mature.

Techniques already in use can later be ported directly onto quantum computers once the hardware is ready, extending their value beyond the current transition period.

“Cost is one of the biggest misconceptions,” he said. “It’s probably not as costly as most people think.”

He said organizations tend to fall into one of three broad postures, depending on how much risk they are willing to carry before the technology matures:

• A strategic pause, watching developments before committing

• An approach akin to an insurance policy, spending modestly without falling behind

• A call-option approach, investing more heavily despite the risk it does not pay off

Companies can use these techniques without training anyone on quantum computing at all.

“I would view that as something of a lost opportunity,” he said.

He said the main costs are talent and hardware access, whether staff is retrained internally or specialists are hired externally.

Recent industry announcements suggest the timeline for enterprise-ready quantum computing is shrinking faster than expected. The techniques themselves take several forms. Quantum feature engineering, for instance, is a data-preprocessing tool for machine learning models, adapted from principles in quantum science to run on today’s classical infrastructure.

Buchholz compares it to a turbocharger: connected to a machine learning engine, the tool is designed to improve outcomes.

A second technique replaces Monte Carlo simulation for pricing derivatives and calculating value at risk.

Monte Carlo estimates outcomes by sampling potential scenarios millions of times. Deloitte’s alternative instead computes exact distributions, which Buchholz said can catch black swan tail risks that sampling-based methods sometimes miss.

Booth noted the technique could be especially useful given current market volatility.

Buchholz said Deloitte’s tools are only part of a wider wave of experimentation across the industry, with other quantum-inspired techniques being developed elsewhere.

Fraud detection pays off

Fraud detection is one of the clearest returns so far. Quantum feature engineering is particularly effective at spotting fraudulent transactions, since even modest accuracy gains carry significant financial weight.

Accuracy improvements of 20% to 40% are not uncommon in fraud detection, according to Buchholz.

He said companies rarely publicize the results, since talking publicly about a fraud problem can simply invite more fraud to deal with. Client confidentiality around fraud detection is common across the industry.

He added that the technique is already in production in a growing number of organizations. Governments are moving toward similar tools. Public sector fraud tends to be large in scale, driven by people pursuing large pots of taxpayer money, and agencies are increasingly focused on catching honest administrative errors as well, which can be just as costly to correct if left unaddressed.

In pharmaceuticals, some of the same computational methods are still being tested for scientific calculations, though results there remain preliminary, Buchholz said. Adoption still varies widely by region, based on the pattern of client requests reaching Deloitte.

“At the vast generalization level, the US is probably more adventuresome, the Japanese are quite adventuresome, the Europeans are somewhat adventuresome, and then there are little pockets here and there for everybody,” Buchholz said.

He also draws a distinction between the underlying quantum hardware, once it matures, and the quantum-inspired techniques described above. Bringing in an actual quantum computer will resemble an infrastructure decision, closer to acquiring a supercomputer or high-performance computing cluster than to deploying an application like generative AI. That is a difference in how companies should organize the adoption, not a reason to hold back.

"A quantum computer is a little bit more like, from an enterprise adoption perspective, having a supercomputer or a high-performance computing cluster," he said.

With inbound interest climbing and industry announcements accelerating, more companies are likely to move off the sidelines toward a no-regrets strategy in the coming year, pursuing quantum-inspired techniques now while preparing for the quantum systems still to come

Quantum computing has spent a decade chasing bigger qubit counts. A recent panel of hardware specialists suggested the industry may already be past that.

Three specialists were asked to name the single metric that matters most. All three circled back to the same idea.

“I think it’s the fidelity of the gate between two qubits,” said Pierre Desjardins, co-founder and chief executive of C12. “This is really what you measure, and it integrates a lot of things that need to be right if we want to scale quantum computers.”

Guillermo Albareda, co-chief technology officer of IDEADED, agreed with him.

“Fidelity is definitely something you need to care about,” he said. “It’s not only about average fidelity, but it’s also about worst-case fidelity, because worst-case fidelity tells you about the bad qubits that are going to spoil the entire circuitry.”

“How many physical qubits you’re going to need to get one logical qubit depends a lot on your connectivity,” he said. “Targeting all-to-all connectivity is an important task.”

“Velocity of the operations, how fast you can implement the two-qubit gate or single-qubit gate, is very important,” he said. “Decoherence basically spoils your information very, very quickly.”

Ellen Devereux, quantum computing chief technology officer advisor at Fujitsu, named a different metric.

“I’m interested in the logical qubit number,” she said. “It’s a bit of a cop-out of an answer because it takes into account the fidelity, the error rate and the connectivity of the qubits, as well as their speed.”

Her preferred figure still comes down to fidelity, combined with the other factors Albareda listed.

All three were describing the same underlying quantity, fidelity, whether reported on its own or folded into one composite number.

New yardsticks for scale

The exchange took place at the Commercialising Quantum Global 2026, an event organized by Economist Enterprise in London. The panel on hardware metrics was moderated by Jason Palmer, host of The Economist’s “The Intelligence” podcast.

C12 is a French startup building qubits from carbon nanotubes designed to work without sitting physically close together. Fujitsu’s quantum computing work combines superconducting and diamond spin qubit research. IDEADED, based in Barcelona, focuses on quantum control techniques that speed up qubit operations.

“We are the first hardware provider to publish the watts per physical qubit, how many watts you will use for the whole system, including the cryogenics needed to run the computation,” Desjardins said.

“The second metric is how many qubits you can pack per square meter, which is a very good proxy for the size of your quantum system,” he said.

He said Panopeia would need about 17 square meters, roughly the size of a few server racks.

“I encourage other quantum providers to share these kinds of metrics in their roadmaps,” he said. “The number of qubits, the number of logical qubits and the logical error rates are important, but these two metrics really show whether a technology will be deployable and commercially viable.”

“The megaquop, gigaquop measurement is starting to get thrown around, but we haven’t clearly defined what a quop is,” Devereux said.

She said a “quop” is shorthand for a single quantum operation performed within one coherence cycle.

“It’s made it into the United Kingdom’s quantum road map, but it’s not clear to me whether that includes error correction or just logical operations,” she said.

Fujitsu’s roadmap links many separate machines together.

“It’s a key part of our roadmap,” she said. “Our current machine is 256 qubits, and the next machine will be four of those chandeliers in one fridge, and the machine after that will be 10 of those fridges connected together with photonic links.”

She said Fujitsu is pursuing both superconducting and diamond spin qubit research in parallel.

“I think it’s relatively well understood that we will need new ways of connecting things together to get from physical qubits to logical qubits to useful quantum computing,” she said.

C12 takes the opposite approach, packing more into a single cryostat first.

“Before scaling out by replicating quantum processing units, we need to spend a lot of effort first on scaling in, on miniaturization,” Desjardins said.

He said C12’s roadmap adds qubits while staying inside the same cryostat, avoiding a new cryogenic unit each time.

Both companies agreed on one thing. Connecting hardware efficiently, not raw qubit counts, would decide who reaches useful quantum computing first.

The 10,000 qubit question

“There are very few algorithms that demonstrate a real advantage over classical computing,” Albareda said. “There are four main algorithms that we know of, and all the rest are basically ramifications of those four.”

He said real advantage comes down to controlling physical qubits, and ultimately how many logical qubits a system can encode.

“From the latest literature, we know it’s at least 10,000 logical qubits, and a few hundred thousand operations in terms of gate depth, not circuit depth,” he said. “We still need better fidelity, better physical qubits. That’s the conclusion.”

“The latest Shor’s algorithm is in the range of 2,000 to 5,000 logical qubits, so it’s a little below that benchmark of 10,000, but it’s of a similar order of magnitude,” Devereux said.

“If we manage to have a fault-tolerant computer, the size at this stage is not going to be relevant,” Albareda said. “I think it’s more relevant the energy it consumes. We can fight the hunger of artificial intelligence (AI).”

He said a quantum computer will likely stay roughly 90% classical hardware plus a quantum accelerator.

“It’s obviously a concern, especially when we’re hearing about AI data centers using huge amounts of energy, but it’s not a comparable metric,” Devereux said. “They’re not working on the same scales as AI data centers.”

“If we think about the things we’re expecting quantum computers to do, building new carbon capture materials, improving battery technology for electric vehicles, the net positive outcome of that computation is far exceeding the amount of energy going into it,” she said.

The panel’s sharpest disagreement was over a related figure, the ratio of physical to logical qubits, and what it actually signals about quality.

“Our roadmap includes 40 physical qubits to one logical qubit as a ratio,” Devereux said.

“That still requires a high level of qubit fidelity as a benchmark. If you don’t have that level of fidelity, error correction doesn’t work,” she said.

"It's actually a ratio you can play around with a lot," Desjardins said. "What's important is to ask what this ratio is for the megaquop or gigaquop regime."

He said C12’s own roadmap for Panopeia is working with a ratio close to 100 physical qubits per logical qubit, efficient only because the system is designed to deliver millions of error-free operations.

“We started out with a metric of quantum volume, and that metric has kind of disappeared already, because we recognized as an industry that it wasn’t a good metric for the commercial or technical audience,” Devereux said.

“Finding standards too early might be counterproductive,” Albareda said. “We need to keep calm and allow new paradigms and platforms to arise that aren’t tied to the standards we might define today.”

The panel never settled on one answer. All three agreed the metrics chosen now will shape what gets built next.

IonQ, a Maryland-based quantum computing company, has spent the past two years turning itself into a full quantum platform, pushed beyond hardware by customer demand and fears over so-called Q-Day.

The shift reflects a broader consolidation sweeping the quantum industry, as hardware, control systems and software converge into one ecosystem.

“If we were here 18 to 24 months earlier, I would just tell you that IonQ is a trapped-ion quantum computing company,” said Mihir Bhaskar, senior vice president of global research and development at IonQ. “We’ve changed a lot during this period.”

“We’ve been pulled into the quantum cybersecurity and networking space because our quantum computers have the possibility to scale and interconnect over networks,” he said. “Because our technologies also have applications for sensing and precision timekeeping, we’ve been pulled into quantum sensing.”

Bhaskar said the pull came from two directions at once: IonQ’s own technology, and customer concern over so-called Q-Day, the point at which powerful quantum computers could break today’s encryption standards.

“We’re starting to see an ecosystem forming,” he said.

“There are hubs where quantum hardware is being deployed, used and accessed, and there are multiple vendors appearing at these hubs.”

“Through all of the investment we’ve made in research, development and hardware, we’ve also created a lot of infrastructure we realize is very useful to the entire industry,” he added. “These are all things that IonQ now does as part of one quantum platform.”

IonQ’s 2025 acquisitions of Lightsynq and Oxford Ionics were aimed at building photonic interconnects and higher-density ion traps.

The deals feed a roadmap that targets 10,000 physical qubits on a single chip by 2027 and more than 2 million by 2030, and they follow a hybrid quantum-classical drug development workflow with AstraZeneca, Amazon Web Services and Nvidia that achieved a speedup of more than 20 times over previous benchmarks.

He said it is a sign of the industry’s growing maturity that companies are now focused on this connective tissue between technologies.

Not backing off

Bhaskar made the comments in a fireside chat with Tom Standage, deputy editor of The Economist, at the Commercialising Quantum Global 2026, an event organized by Economist Enterprise and held in London to discuss the state of quantum infrastructure.

The session examined quantum computing’s “missing middle,” the connective infrastructure needed to make the technology commercially reliable.

Bhaskar leads IonQ’s global research and development function. He previously worked at Amazon Web Services (AWS), where he watched cloud infrastructure scale rapidly to support the artificial intelligence (AI) boom, an experience he later drew on to describe quantum’s current stage of growth.

Standage asked whether the platform push signaled a retreat from trapped-ion technology, IonQ’s original specialty, in favor of diversification.

“It’s definitely not a retreat. We’re spending more and more resources on trapped-ion quantum computing,” Bhaskar said. “We’re manufacturing more systems every year, both internally for engineering prototypes and for systems deployed to customers and on the cloud.”

He said the expansion reflects growth of the quantum market rather than a retreat from core hardware.

“The quantum industry emerged from the lab over the previous decade, buying general-purpose components from cottage industries and putting them together to make our first prototypes,” Bhaskar said. “We now have a volume of interest in these quantum computers that outpaces the rate and scale at which we can manufacture using those cottage industries.”

He said that scale-up depends on manufacturing capacity built largely by hand today, which cannot keep pace with rising demand.

“There’s certainly a need to scale up the manufacturing of these systems,” he said. “Where that starts is in the semiconductor backbone.”

IonQ has moved to secure its own place in that supply chain.

“The foundries have to evolve,” Bhaskar said. “Most recently, in January, we announced our planned acquisition of a company called SkyWater, a US-based foundry with locations in Minnesota, Texas and Florida.”

The acquisition closed on July 31, following required regulatory approvals, under terms giving SkyWater shareholders $15.00 in cash plus 0.4883 IonQ shares for each share held. The combined company held its first joint earnings call in early August and plans an investor day in the third quarter of 2026.

The deal creates what IonQ calls the only vertically integrated full-stack quantum platform company.

IonQ, listed on the New York Stock Exchange and headquartered in College Park, Maryland, says it achieved a record 99.99% two-qubit gate fidelity in 2025 and now operates across the United States, Europe and Asia, including the United Kingdom. SkyWater continues to operate as a subsidiary, led by chief executive Thomas Sonderman, who now reports to IonQ chairman and chief executive Niccolo de Masi.

“SkyWater doesn’t build just trapped ions. They have a platform for superconducting routing and a platform for photonics, so it isn’t exclusive to trapped ion manufacturing,” Bhaskar said.

He said the improvement plan extends to production speed as well.

“Most semiconductor companies lock in a design and it takes six months before they get the chips back to verify whether the design worked,” he said. “Our goal is to shorten that cycle with SkyWater to three weeks.”

Open by design

IonQ’s hardware is already reaching customers through both routes.

“Just with an AWS account, Azure or GCP (Google Cloud Platform), you can access these machines,” Bhaskar said. “That’s been incredibly important for getting access, and for showing that we can build many generations of hardware that we’ve had on the cloud.”

“These are real systems that turn on. These aren’t just things that we put in PowerPoint slides,” he said.

The company has also sold hardware directly for on-premises use, generally deploying its newest machines there before making them available on the cloud. IonQ has sold several Tempo systems, its 100-qubit machines, and recently announced the first sale of a next-generation 256-qubit system, also for on-premises deployment.

On the networking side, IonQ partners with the US Defense Advanced Research Projects Agency, or DARPA.

The partnership centers on a US government program called the Heterogeneous Architectures for Quantum (HARQ), an effort to design a single networking standard broad enough that every quantum computing vendor, regardless of hardware type, would eventually need to adopt it.

“This is about designing the networking standard that all quantum computers and devices are going to have to connect through,” Bhaskar said.

Standage noted that DARPA has a track record of establishing earlier networking standards, and Bhaskar agreed.

He compared the industry’s current stage to the early years of graphics processors, before they became the backbone of AI computing.

“The analogy I like to tell, which is fresher in our minds with AI, is where Nvidia was when AI was an interesting research problem for researchers running one or a few GPUs,” he said. “These GPUs were curiosity devices for gaming and Bitcoin mining, and for AI researchers doing kind of weird stuff, in the early 2010s.”

Bhaskar said he watched that shift happen firsthand during his time at AWS.

“When those technologies were able to be deployed in the data center en masse and connected together and manufactured at scale, that compute power became available to support AI,” he said, adding that software, particularly quantum error correction, is a very important part of the platform.

“Nothing is as simple as all that. We are talking about quantum computing, after all, but the goal is to make it simple and accessible for end users,” Bhaskar said.

IonQ expects that work, along with continued foundry investment, to determine how quickly quantum computing becomes dependable infrastructure.

Quantum computers cannot become useful on hardware alone. The physical qubits at their core are inherently noisy, and without constant correction they produce little more than static.

The fix lies in software-defined logical qubits, abstractions built on top of physical hardware that require constant, real-time artificial intelligence (AI). Errors must be decoded and corrected, and noise calibrated, on microsecond time scales, or the system collapses back into noise.

“It’s AI that can help make our logical qubits sing,” said Krysta Svore, vice president of applied research for quantum computing at Nvidia. “Without AI in the loop, you just have noise.”

“There is no useful quantum future without AI and intelligence integrated into our qubits,” she said.

The company’s answer is Nvidia Ising, a family of open models built to decode and calibrate logical qubits at scale. A 35 billion parameter vision-language model handles calibration, tuning a quantum computer in hours instead of weeks or days.

A second model, a 2 billion parameter convolutional neural network, performs the decoding. Nvidia says it runs two and a half times faster than earlier approaches and delivers a threefold improvement in logical error rates, both central to the reliability of logical qubits.

“The upgrade isn’t just a quantum machine,” Svore said. “It’s also a modern stack for science that integrates AI and quantum together, so that they strengthen each other.”

Nvidia has released the models, their data sets and benchmarks openly, letting hardware makers and research labs fine-tune them for their own qubits rather than starting from scratch. The models are already in use across the quantum ecosystem, from national laboratories to commercial hardware makers.

Svore said the goal is a foundation the wider ecosystem can build on, rather than a closed product, one piece of a platform Nvidia intends to keep expanding as more of the convergence between AI and quantum takes shape.

A new science stack

The quantum computing scientist made the case for this convergence in a keynote at the Commercialising Quantum Global 2026, a conference organized by Economist Enterprise in London that focused on the path to practical quantum computing.

The two loops of modern research, physical simulation and data-driven learning, are converging into a single stack spanning simulation, AI and autonomous discovery agents.

“For 400 years, science has really had one loop: observation, theory, experiment,” Svore said. “Now we’re running another loop on top of that.”

That second loop runs on data at a scale never available before and on models capable of reasoning. The resulting stack rests on three converging capabilities:

• Simulation, accelerated to produce first-principle models

• AI, which produces learned models from data

• Agents, a new layer that ties the two together

“This isn’t just a faster science,” she said. “I’d argue it’s a different type of science, one where the instrument, the model and the experimenter start to merge and become one.”

“Cycles that took years can now take weeks or even days or even seconds,” she said.

That compression is driven largely by autonomous agents, systems capable of running entire research cycles without a person in the loop. She said it marks a complete workflow upgrade, one built for agents that increasingly talk to other agents rather than waiting on human review.

“These agents in science aren’t just a chatbot,” she said. “It’s a system that proposes a molecule, simulates it, evaluates it, reasons over the search space, and then proposes the next one without human intervention, autonomously.”

“Organizations that don’t build for an agent-driven loop will be out-cycled on discovery time,” Svore said.

Early gains from agent-driven discovery are expected across materials science, climate research, chemistry and medicine, she said.

Quantum as instrument

A scientific instrument is only as useful as our ability to interpret what it reveals. Quantum hardware produces data that classical, digital systems cannot efficiently collect, manufacture or generate on their own.

“Galileo had the telescope. We have quantum devices,” she said. “AI and quantum are not two separate stories. They’re one. Quantum produces data, AI reasons over it.”

That data can be used to train and fine-tune AI models, sharpening predictions in chemistry, materials science and medicine, fields she says classical computing alone cannot reach.

“There is no separate quantum problem,” she said. “At the core, scientific problems need quantum to be solved.”

Connecting that instrument to Nvidia’s AI infrastructure required a new piece of hardware. The company built NVQLink, an open, low-latency interconnect linking graphics processing unit (GPU) supercomputers to quantum processors.

“We can think of NVQLink as bringing real-time AI to qubits, enabling the decoding of them and the stabilizing of them,” Svore said.

The link is designed to run on the microsecond time scales needed to correct and stabilize a logical quantum processor, connecting it directly to supercomputers running CUDA-Q, Nvidia’s platform for programming across classical and quantum hardware as one system.

The design lets organizations program across CPUs, GPUs, quantum processors and other accelerators as a single addressable system, reserving quantum processors for the tasks only quantum can do and for producing the specialized data that feeds back into training AI models more broadly.

“Integrating GPUs and AI really means scaling up to millions of operations and hundreds of logical qubits,” she said.

She said the work ahead goes beyond the qubits themselves, to include data architecture, engineering talent, workflows and hybrid applications. The hardware will be there. The open question is whether the ecosystem can use it.

“The speed of science today is a function of how many minds can touch the stack,” she said.

Reaching that scale depends on keeping the platform open. Progress requires open models, open data sets and open benchmarks so every developer and every type of qubit can participate.

“Open isn’t just a philosophy. It’s an engineering choice that determines how fast science moves,” she said. “Whether you’re a pharma company, a national lab or a graduate student, we all should be building on the same foundation. That’s how progress compounds.”

Nvidia says the next milestone is scaling from today’s noisy processors to systems running hundreds of logical qubits and millions of operations, a shift the company expects to unfold as its open models and interconnects mature across the wider quantum ecosystem.

The odds of quantum computers cracking one of the internet’s most trusted encryption standards within a decade have jumped sharply in just a single year.

New estimates suggest it would take roughly 26,000 qubits to break RSA-2048, the encryption standard protecting much of the world’s financial data, down from earlier projections that ran into the tens of millions. The shift signals that a “cryptographically relevant” quantum computer may arrive well before many institutions are ready.

“PQC (post-quantum cryptography) and quantum-safe security is a very significant threat that quantum poses to all sectors, particularly financial services, which are top targets for cyber criminals,” said Philip Intallura, global head of quantum technologies at HSBC.

“There’s now a 38% chance of a quantum computer breaking RSA in the next 10 years, and that’s based on a survey of deep experts that runs every year,” he said. “That probability has increased 12 percentage points over the last year alone.”

He said the qubits needed to break RSA-2048 have fallen from an estimated 20 million five years ago to the tens of thousands today, a shift he called non-linear progress in algorithmic efficiency.

The chart he presented used a logarithmic scale, meaning the real shift is steeper than the numbers first suggest.

“When you think about the consequence of not being able to keep customers’ data and payments secure, that is not a comfortable probability,” Intallura said. “A breach of security at our bank would result in a catastrophic loss of confidence from our clients.”

He said he was glad HSBC started work on the cost of quantum-safe security four years ago, rather than only beginning now. He focuses on a 10-year horizon, roughly how long a bank HSBC’s size takes to modernize its cryptography.

He added that some qubit architectures remain far harder to build than others, so falling counts alone do not fix an exact date, and noted the same warning had been raised earlier at the conference by Lord Holmes.

HSBC has run a dedicated quantum technology group for nearly five years, spanning work in trading, security and tokenization. Intallura said the case for taking quantum seriously is far easier to make today than when he started, given how quickly the numbers have moved.

Proof already paying off

His remarks came during the Commercialising Quantum Global 2026, organized by Economist Enterprise, in London. The session explored how industries can capture near-term value from quantum without waiting for fault-tolerant machines.

One example: a pilot HSBC ran with IBM last year on algorithmic trading to price corporate bonds.

“If you price too aggressively, you’re likely to win the trade, but it costs you margin,” Intallura said. “If you price too conservatively, you won’t be competitive, so you won’t win the trade.”

The pilot applied quantum machine learning to the feature-generation stage of the pricing model. It condensed 216 classical features into 109 qubits run on an IBM Heron processor, before feeding the results into classical models including logistic regression, XGBoost (eXtreme Gradient Boosting), random forest and neural networks.

The approach delivered a 34% uplift in accuracy on real quantum hardware compared with classical methods.

“This is not a claim of quantum advantage; it’s a claim of beating models that we’ve already got,” Intallura said. “What was interesting is we used a real quantum computer with a real data set at production scale.”

HSBC called it an empirical finding, not a theoretical claim of quantum advantage.

The result decays over time and needs retraining, though it continued to beat HSBC’s classical baseline for a few days even without fresh data. Intallura said the bank is now in an early phase of building a pilot that touches its live trading systems, extending the work beyond the initial proof of concept.

“Quantum is becoming a useful tool in the toolbox as we develop these types of models within the bank,” he said.

HSBC’s quantum credentials extend beyond a single pilot, ranking second among banks on the Quantum Insider’s index.

Behind only JPMorgan Chase, it ranked ahead of Goldman Sachs, Wells Fargo and Citi. On Evident AI’s Quantum Leaderboard, HSBC sits among the “strong seconds,” behind trailblazer JPMorgan Chase.

“HSBC is positioned very well in those rankings, and it’s not because we’ve ever demonstrated quantum advantage,” Intallura said. “That generates a lot of brand value for us. And it is absolutely not true to say that there is no return on investment, near-term return on investment, from quantum.”

He pointed to grant access as an end user in startup-led experiments, and to requests from relationship managers serving HSBC’s largest corporate clients who want to learn from the bank’s quantum program as they build their own.

“The most tangible benefit is acquisitions into HSBC Innovation Banking,” he said. “We attract hundreds of millions of dollars into our innovation bank through quantum and innovation startups that have raised Series A, B or C funding and have global banking needs.”

Betting on early movers

The scale of the wider opportunity adds to the urgency. McKinsey estimates quantum adopters could unlock $1.2 trillion to $2.7 trillion in value over the next decade, with financial services capturing $400 billion to $600 billion of that.

“There is value to be captured,” Intallura said. “Financial services is one of the top beneficiaries of that value.”

“Our bias has been over the last four years to what can we do with quantum technologies of different flavors, like annealing, simulation or tensor network elements,” he said. “What we’re trying to do is not achieve quantum advantage, but achieve marginal commercial advantage by beating models we’ve already got.”

He said that approach builds organizational readiness, demonstrates real progress rather than a research paper about the future, and keeps investment flowing toward eventual fault-tolerant systems.

“Quantum doesn’t solve everything,” he said. “It’s a very narrow sliver of problems that it can help accelerate and improve.”

He said building a quantum capability inside a bank starts with assembling a team, a strategy and funding, before any project begins. From there, a project moves through:

• Business engagement, to understand the computational challenge

• Problem identification and scoping, including whether quantum can help at all

• Data identification, sourcing the right data to use

• Vendor selection and procurement

• A pilot touching production systems, if the business case holds up

“It’s materially easier to make the argument for quantum investment now than it was five years ago, when I got a lot of strange stares given the state of the technology at that time,” he said.

Every quantum use case and research paper HSBC has produced has involved a different partner: IBM on algorithmic trading, Fujitsu on portfolio optimization, Quantum Dice on stochastic modeling, Quantinuum on gold tokenization, and BT and Toshiba on quantum key distribution for foreign exchange.

Research partners also include HAIQU, Classiq, CTU, Anyon, Imperial College London and the Hong Kong University of Science and Technology.

“No one can do this alone; it’s an ecosystem of collaboration,” Intallura said. “We’ve even done work with regulators and government, so we’re in this together.”

That scale of investment comes wrapped in some of the tightest export controls of any emerging technology.

Global committed spending on quantum technologies topped $60 billion as of October 2025, led by China at $17 billion, the United States at $7.7 billion, Japan at $7.1 billion and the UK at $6.7 billion, underscoring how much government money is now chasing the technology.

He pointed to the Quantum Growth Alliance, announced the week before his talk by UK Science Minister Lord Vallance, as one of the clearest signals yet that end users are organizing around the technology.

Founding members include HSBC, Barclays, Standard Chartered, GSK, BP, Rolls-Royce, BAE Systems, BT, Vodafone and QinetiQ.

“This is one of the strongest signals we’ve seen to date in trying to build up the end users, large UK, globally recognized leading institutions, getting together and working out how we, as a group, are going to have a pathway to use the technology,” Intallura said. “In quantum, the winners will be the organizations that move before they have to.”

HSBC’s own pilots remain in an early phase. Intallura said the next milestone is pushing the bond-trading model into systems that touch live trading, while the bank continues preparing its cryptography for a decade-long migration to quantum-safe standards.

The quantum computing industry is racing to build specialized chip factories, without agreeing on what one should even be.

Different qubit technologies, from superconducting circuits to photonic and ion-based systems, draw on overlapping tools for sensing and quantum communications. Much of what lies beneath them turns out to be existing photonics and semiconductor hardware, and quantum startups are already funneling large shares of their funding into foundry costs.

“We haven’t even defined what a quantum foundry is in this conversation yet,” said Joe Spencer, UK director of Global Quantum Intelligence (currently director of IoniQ Consulting). “There needs to be some conversation of what that actually looks like at a global scale.”

He said most of the shared technology beneath different compute modalities is largely existing photonics and semiconductor hardware.

“We haven’t got a modality that wins out yet, so I think we should be agnostic to all of them for a foundry model,” he said.

Spencer’s firm has assessed about $5 billion in quantum investment. His focus is resilient supply chains for defense and security.

He said even within one modality such as superconducting qubits, a shift to new materials such as sapphire to extend coherence lifetimes could force a rebuild of the entire materials-processing pipeline. He said materials, components and people remain key supply chain parts, and a shared foundry model could support talent, job creation and specialized processing.

“The best tech is not necessarily the best investment,” said Manjari Chandran-Ramesh, partner at Amadeus Capital Partners. “Execution and agility matter just as much as having the best technology.”

Her firm, Amadeus Capital Partners, has invested in 200 companies and exited 100 over three decades.

“A number of our companies are spending a humongous amount of what they raise on foundries,” she said. “That is a big chunk, comparable to several full-time employees in the quantum business.”

Her portfolio includes the quantum firms Photonic, New Quantum and the error-correction specialist Rebellion.

“It’s great that there is GlobalFoundries, there is CEA-Leti and so many other foundries, but for whatever reason they get a very big chunk of money from the company,” Chandran-Ramesh said.

Betting on one technology

The panel was part of Commercialising Quantum Global 2026, an event organized by Economist Enterprise in London. Christopher Bishop, chief reinvention officer of Improvising Careers, moderated the discussion on who will own the infrastructure behind the next generation of quantum computing.

He opened by asking how a new foundry model might reshape the wider quantum economy.

Ann Dunkin, a distinguished professor of the practice at Georgia Institute of Technology, is a former chief information officer at the US Department of Energy.

She told the panel that Washington’s approach to funding one specific quantum foundry breaks with historical precedent.

“All of them involve the US government taking an equity position in those companies, and that is historically not something the US government does when it invests,” Dunkin said. “We invest to move the US economy forward. We do not invest to make money.”

Dunkin said there were nine such investments in total, and only one was a foundry.

The rest are companies developing quantum computers or related technology. The Commerce Department’s letters of intent, signed in May, are worth a combined $2 billion, according to public records.

Anderon itself is an IBM spinoff backed by $1 billion in CHIPS Act incentives, matched by IBM.

“Anderon is supposed to be superconducting first, and then other things. That is what they have promised,” Dunkin said. “Will it actually be more things, or have we in fact put our thumb on the scale for superconducting?”

“The typical government behavior is to make bets on multiple technologies, so it is unusual to see a billion dollars for one particular technology,” she said. “I find the scale of that one investment a bit concerning without others being made.”

Dunkin said the Department of Energy runs a separate effort focused on artificial intelligence (AI), called the Genesis Mission.

“Genesis sees quantum as an accelerator for AI, and I think that’s the right answer,” she said. “Quantum high-performance computing and AI will be a set of tools we use together to accelerate a lot of important research.”

The scramble for talent

Dunkin said it is entirely possible the quantum industry ends up concentrated in a handful of foundries worldwide.

“The United States is very good at innovation. We are not very good at scaling things, and we need to get in early to scale,” she said.

She said China is far better at scaling, and the West risks repeating losses already suffered elsewhere.

“We should be stapling a green card to the diploma for every single person we graduate from university who is not a US citizen,” she said.

Spencer offered a UK view.

“Imagine being in the UK, where we’re good at innovation but not so good at commercialization or scaling,” he said. “We have the roadmaps and the plans to execute on this, and we are doing it.”

Chandran-Ramesh said Europe has an opening created by the US-China rivalry, pointing to an existing foundry ecosystem built around Imec in Belgium, CEA-Leti in France and VTT in Finland, all backed by decades of public and private investment in advanced manufacturing.

“Europe can come out as one of the key players here,” she said.

“Export controls are part of life. There is a law of unintended consequences. If you put restrictions on something, other nations will develop their own capability,” Spencer said. “Dual use is not binary; it is a spectrum, and we should monitor how it evolves.”

Ching-Ray Chang, a board director at Foxconn, also directs the Quantum Information Center at Chung Yuan Christian University in Taiwan. He said the island’s own semiconductor history carries a warning for the quantum race.

“In 1988, I saw a lot of female workers come by bus every day. I asked them who they were and where they were going. They said TSMC. I had never heard of that,” Chang said. “Nobody expected that after 40 years TSMC would become so successful and become the backbone of the electronics industry.”

“Kodak was doing very well in the film business and had even invented the digital camera, but it never pushed that lab research into industrial application,” he said. “After 40 years Kodak disappeared, but TSMC did not.”

“Taiwan is slow in quantum technology because most of our talent and money is invested in semiconductors,” he said. “We don’t have enough talent for quantum technology.”

Chang said Taiwan is racing to catch up.

Heavier classification of quantum research now makes international collaboration harder than it was in the early days of semiconductors, he said. The coming year, from Anderon’s build-out to Europe’s push for a bigger share of the supply chain, should show whether the industry can scale as fast as it innovates.

Turning quantum computing into an everyday business tool will hinge less on faster chips than on whether employees trust the people building the systems around them.

That trust has to be earned deep inside a company’s value chain, quantum experts said.

“As a method developer, it doesn’t really matter whether your new shiny method is quantum or classical,” said Christian Gogolin, head of high performance and quantum computing at Covestro. “You need to first convince the practitioners that your method is superior to the methods that have been there previously.”

He said the persuasion chain runs from computational scientists to lab chemists to the application team and finally sales.

“You need to build this culture of trust and of relationships,” he said. “Otherwise you will not be able to profit from any innovations coming in at the bottom of this value chain.”

He said trust takes years to build, because people need to personally experience the advantages of a new way of working before they believe in it, and that organizations building this readiness now, using other technologies as practice, will barely need to change anything once quantum computing matures enough to be useful.

“Ideally, when quantum day (Q-Day) arrives, there shouldn’t be any need for any change in your organization anymore,” Gogolin said. “You should have processes and tooling ready into which you can just plug quantum computing in.”

The stakes of waiting are high.

“The risk we’re trying to mitigate by participating in the technology development of quantum computing is the risk of being left behind and potentially overtaken by the hyperscalers,” Gogolin said. “That would be catastrophic for the industry if we were to be degraded to a pure workbench while someone else does the innovation.”

Other panelists framed the same challenge differently.

Miryem Salah, former director of digital, data and transformation at VodafoneThree, said the same discipline applies to any technology decision, not just quantum. She said organizations need to clarify their strategy and team structure first, before asking whether the right tool is pen and paper or quantum computing.

“Most organizations are set up in a way where technology is split from other areas,” Salah said. “They need to work very hard bringing people on the journey. It shouldn't be an us-and-them [mentality] between technology and everybody else."

That divide, panelists agreed, slows quantum adoption more than the hardware’s immaturity.

Proving value first

The panel, titled “From Pilots to Platforms,” examined what changes for organizations once they reach quantum advantage. It was held in London at Commercialising Quantum Global 2026, organized by Economist Enterprise and moderated by Charlotte Bullard Davies, senior manager for primary research at Economist Enterprise.

Panelists said winning budget for quantum projects starts with proving the business case rather than the science.

“This is definitely the key priority. You need buy-in on all levels of the stack, from executives to management to the engineers and scientists actually doing the work,” said Corey O’Meara, chief quantum scientist at E.ON.

O’Meara said E.ON ties every use case to a clear business potential.

“We’re all scientists, but we’re trying to drive business value,” O’Meara said. “The end results were patents, federal grants and EU grants, to show that in the competitive landscape of Europe we’re really towards the cutting edge.”

He said quantum computers today are still research machines, so the focus now is algorithm development.

“From a C-suite perspective, fear-of-missing-out (FOMO) is a great catalyst for getting moving,” said Dave Starling, head of future products and technology at NatWest Group. “Even if that doesn’t run the whole conversation, it’s a good catalyst to start it.”

That urgency meets a harder problem at NatWest.

“If quantum advantage hit tomorrow, would any organization be ready? I don’t think so,” Starling said. “They would lack a lot of the structures that traditionally sit in a large enterprise to support AI rollouts.”

He said the questions run deeper than compliance forms.

“What does data mean when it comes to quantum? What is security? Is there such a thing as quantum PII (personally identifiable information)? I don’t think we’ve got those answers yet,” he said.

NatWest applies a framework called model risk to evaluate whether any algorithm, quantum or otherwise, is deterministic, explainable and secure.

Starling said that process became a bottleneck after the arrival of ChatGPT sent a wave of AI projects through the bank’s review pipeline, and he expects quantum to create similar strain.

Because NatWest has no quantum hardware of its own, every job runs on cloud infrastructure.

“We’re sending a transpiled Qiskit circuit with quantum rotations and spins, but that’s really hard to relate to a security team,” he said.

“In order to productionize a quantum system, we have to adapt the existing security and model risk processes we have to the quantum platform,” he said. “It’s not easy, because you’re speaking in terms that people just don’t naturally understand, but that’s going to be a big challenge for us over the next few years.”

The coming compute scramble

As the economics of quantum computing shift, panelists said the real return on investment (ROI) will not be measured in scientific papers.

“Today we don’t have quantum computers big enough to generate real ROI, so the ROI is things like subsidies and patents right now,” said Matthijs Rijlaarsdam, co-founder and chief executive of QuantWare. “Once we get to useful systems, convincing management will be about economics, like how much compute I get for my dollar.”

He said the top metric will be compute-per-dollar, whether on GPUs, QPUs (quantum processing units) or CPUs, followed by the reliability of a company’s compute supply chain, which will become a deeply strategic asset.

“Classical compute and AI improved gradually, from small CPUs to bigger chips, GPUs and clusters,” he said. “Quantum is much more a singular moment. At some point these systems will be big enough to outperform classical computers exponentially, and they will have to provide more compute per watt than GPUs.”

Even before the economics are settled, panelists said the more basic problem is knowing which problems quantum computing should solve.

“Use cases are probably the biggest block right now,” Starling said. “We don’t have an audit of every algorithm the way we do classically, so we don’t know where we’re compute bound or where there are problems we’ve decided not to solve because they’re intractable.”

“There’s so much happening that we just don’t know what to do, so we push down whatever we don’t see as a risk or an issue straight away,” Salah said.

O’Meara said the search for use cases has shifted from a centralized team to business units emailing ideas directly, a sign that internal training and media coverage have raised awareness of quantum computing across E.ON’s subsidiaries throughout Europe over the past year.

“I can count the algorithms on my hands,” he said. “There’s only so many, so it becomes an education topic.”

Rijlaarsdam said hardware production faces a supply crunch of its own too, similar to the chip and transformer shortages already slowing down AI rollouts.

“The amount of fabs and industrial production capacity we will need will not be able to meet demand,” he said. “If you think that moment will be here in three to five years and you’re not building production capacity now. You’re going to be too late.”

Panelists said the organizations that move first will be the ones that treat quantum readiness, from governance to budget cases to supply relationships, as a priority now rather than something to revisit once quantum advantage arrives.

Financial institutions are quietly racing against a deadline they cannot see coming. When quantum computers eventually become powerful enough to break today’s encryption standards, sensitive data stolen right now could be unlocked in an instant.

Quantum machines capable of that kind of decryption remain years away, but the risk from data collected today is already immediate. Records intercepted now can simply be stored until a machine capable of unlocking them exists, a strategy the industry calls “harvest now, hack later.”

The threat, in other words, comes in two distinct layers: the still-distant question of when a quantum machine will be powerful enough to break codes, and the immediate question of what to do about data that is vulnerable right now.

Banks handle vast volumes of financial data built up over decades, all of it a target long before any quantum computer is switched on. The scale of that exposure is forcing a rethink of how quickly cryptography needs to change.

“If we prepare ourselves for Q day, we are already late,” said Yulia Shamsudinova, head of AI transformation and process intelligence at BNP Paribas. “We need to start right now on this point.”

“We have this term: ‘harvest now and hack later,’ and we do understand that this data might be very vulnerable,” she said. “The cryptography is a total must. It’s maturing very fast, and the funding is there.”

No bank can overhaul everything at once. Shamsudinova said the priority list starts with client-facing platforms and portals, then extends to digital-asset applications and other critical systems used across the group.

That urgency is starting to register beyond the technology team.

“The moment you mention security to our executives, that is when you get their attention,” said Asteris Apostolidis, senior technical innovation lead at KLM Royal Dutch Airlines.

He said it is the first time in eight years of raising the topic internally that executives have shown this kind of traction.

The same anxiety over who controls sensitive data runs through a bigger question hanging over the wider quantum industry. Who ultimately controls the computing power and energy behind it?

Compute turns political

The discussion took place at Commercialising Quantum Global 2026 in London, an event organized by Economist Enterprise exploring how new computing architectures are reshaping the relationship between data, software and computing power. Alex Hern, an AI writer at The Economist, moderated the panel.

“How much energy we can generate to power our systems, how much compute we can build, who can access that compute and where that data lives are increasingly politicized questions,” said Harry Stovin-Bradford, senior director and head of government affairs for Europe at the quantum computing firm IonQ.

The European Union’s Quantum Act, expected by the end of the year, is an early attempt to answer some of these questions, though governments still have significant work to do at a national level.

“What does sovereignty look like from a quantum perspective? Is it the ownership of a system? Is it the ability to direct that system? Is it where the system was built?” he said.

He said compute and the energy that powers it are becoming inherently more national than international, particularly for countries such as Britain and the Netherlands that face constraints on their power grids.

IonQ’s own hardware may have an advantage there.

“Our systems, in particular the trapped ion modality, are significantly less energy intensive than a lot of classical supercomputing, and perhaps less energy intensive than some other quantum modalities like superconducting,” he said.

The same instinct for caution is reshaping how banks think about the data they collect in the first place. For years, the industry default was to gather everything and worry about its use later; executives now say that approach carries its own risk.

“As a large bank in Europe, we take it really seriously. Data protection and data security are among the biggest topics for us,” Shamsudinova said.

She said most of BNP Paribas’s systems are built internally or with partners who work inside the organization rather than on external platforms, reflecting the bank’s caution around cybersecurity.

“Technology is going to apply to organizations, but it’s not going to fix them. If your data is broken or incomplete, you are just going to propagate the fragmentation,” she said.

Only once that foundation, meaning governance and a clear operating model, is solid does it make sense to scale new technology such as artificial intelligence (AI) or quantum computing, she said.

Akshay Pore, managing director for data modernization, AI automation and strategic architecture at Bank of America, joined the discussion via video link.

He described a similar discipline at Bank of America, where data is organized into what he calls data products.

“The data itself is a product that is transacted between different lines of business. We create data contracts around it, treat it as a product, govern that product well, and expose it to AI workloads,” he said.

Solving the scheduling puzzle

Few problems illustrate the promise of quantum computing as vividly as the daily puzzle of running an airline.

“If you strip an airline down to its core, it’s a huge optimization and scheduling problem. You need to combine a pretty extensive network with certain aircraft and crews, under operational, labor and technical constraints, and maintenance,” Apostolidis said.

The goal is for quantum computing to eventually search far more combinations than any classical system can manage today, to find the schedules that actually work.

“That’s a problem no one has managed to solve in its entirety. So what we do is break it down into smaller problems and try to solve them, sometimes successfully, sometimes not,” he said.

“Keep in mind that KLM is the oldest operating airline in the world. We’re 170 years old right now, so that’s a huge benefit, but it comes with lots of legacy as well,” he said.

The airline has run early proof-of-concept projects on crew rostering and, separately, digital marketing, partnering with outside companies, government bodies, research institutes and academia to find where quantum computing actually helps before committing more resources.

That cautious, one-step-at-a-time approach reflects a broader theme among the panelists. Readiness, not raw processing power, is the real bottleneck.

“It’s not about the computational power. It’s more about how ready we are, and which areas we need to be more ready in than others,” Shamsudinova said.

She said she does not believe the future belongs to quantum, AI or classical computing alone, but to a hybrid of all three over the next couple of years.

Pore set a concrete bar for that hybrid future.

“Right now we need to get quantum to a fidelity of at least four nines, essentially 99.99%, before we can integrate it with traditional workloads,” he said.

He said the goal is a layer of abstraction so users never have to know which technology is doing the work.

Even before that threshold is reached, quantum computing is already producing results elsewhere in the business.

“You can use quantum in both pre- and post-training of AI to substantially increase the performance of a large language model against standard benchmarks. That’s promising,” Stovin-Bradford said.

He pointed to a project with an automotive client designing steel alloys, where the models performed far better than earlier attempts.

“The models we found were 70% more useful at the point of delivery than in standard classical large language model (LLM) use,” he said.

Asked whether quantum has become unavoidable rather than a wait-and-see bet, Stovin-Bradford said yes without hesitation.

Apostolidis said executives are showing more interest than at any point in his eight years pitching the technology internally.

Shamsudinova said industry attention still leans toward AI for now, even as cryptography funding accelerates. Pore said the clearest near-term payoff sits in fraud detection, data processing, image processing, key distribution and cryptography, all of which he expects to mature within four to five years.

Each panelist pointed to a different timeline. All the same, quantum computing is moving from a research curiosity toward something banks, airlines and hardware makers alike are actively building around.

Schneider Electric has released a study concluding that arc flash risk in 800-volt direct current (VDC) data center power systems can be managed and, in many cases, matches the safety profile of conventional alternating current (AC) systems, the company said.

The French energy technology firm supplies power distribution, industrial automation and data center equipment and software, including the ETAP power system simulation platform it majority owns.

The findings arrive as 800 VDC architectures gain adoption in AI data centers to support denser server racks beyond 400 kilowatts, a shift driven partly by NVIDIA’s push for higher power AI computing hardware. Unlike AC systems, converter-fed 800 VDC systems currently lack an industry-wide standard for managing arc flash hazards.

The study used ETAP’s simulation software and digital twins to model fault scenarios.

Two 800 VDC architectures were evaluated: a rack-level “sidecar” design and a centralized, facility-level design, both reflecting deployment patterns already used by major hyperscale operators.

The rack-level case study found incident energy remained well below the referenced 1.2 cal/cm² threshold for personal protective equipment (PPE), even without protection devices installed. The facility level design showed slightly higher incident energy under conservative assumptions with no overcurrent protection, though adding standard protection devices brought energy in line with typical AC architectures.

Capacitor discharge dominates the first milliseconds of a fault event, and standard assessment methods tend to overestimate risk in capacitor-dominated systems, the study found.

“800 VDC power distribution represents a significant shift in data center design, but it also introduces safety considerations that need to be studied extensively,” said Manish Kumar, executive vice president of secure power and data centers at Schneider Electric. “Our work with major hyperscalers provides engineers and safety professionals with one of the first practical frameworks for evaluating arc flash risks.”

“Industry standards remain essential for arc flash and electrical safety, but traditional methods can be overly conservative because they do not fully reflect how complex DC systems operate,” said Tanuj Khandelwal, chief executive of ETAP.

The study also found that fault locations upstream and downstream of reverse blocking diodes affected back-feed and peak current, shaping facility-level outcomes. Schneider Electric said the work builds on its history of arc flash safety testing, including validation of live swap power capabilities in 800 VDC systems.

GPS jamming is no longer a rare battlefield tactic. It now threatens airlines and militaries alike.

The disruption costs an estimated one billion dollars a day in the United States alone, and a new generation of navigation technology built on quantum sensors is racing to close the gap.

“GPS jamming is an increasing threat that faces both militaries and commercial aviation,” said Niall Moroney, lead solutions engineer at Q-CTRL. “We developed a sensor and a navigation system that allows pilots to know where they are, even when they’re facing this threat to GPS.”

He said the company’s chief executive had already proven the approach worked outside the lab a year earlier.

“Militaries will necessarily be operating in areas of GPS denial,” he said. “That means those operators find it difficult to know where they are, so there is a strong incentive for them to adopt alternative navigation systems, such as our own.”

That earlier demonstration paired a quantum sensor with Q-CTRL’s own navigation software to solve a real-world problem better than the best available classical alternative.

Q-CTRL is a Sydney-based quantum control company that builds software to make quantum hardware and sensors perform reliably in real-world conditions.

Its navigation product, Ironstone Opal, was unveiled in February as the first commercially available quantum navigation system validated for defense and aerospace use, following extensive trials across air, land and maritime settings that improved inertial positioning accuracy by more than a hundredfold.

The system was later named to TIME magazine’s list of the best inventions of 2025.

Moroney said the company has since made its first commercial sale of the system, though public materials describe the offering as a presale evaluation kit previewed at the Singapore Airshow, allowing customers to test its performance before committing to a full purchase.

It is now working with manufacturers to move Ironstone Opal toward full production at scale.

“We have a strategic partnership with Airbus, where we’re looking to integrate our quantum navigation systems for commercial aviation,” he said.

Airbus Ventures first backed Q-CTRL with a $25 million investment in 2021.

The two companies have since deepened that relationship through dedicated navigation trials, with field tests showing the joint system outperforming high-end inertial navigation systems by up to fifty times under real-world flight conditions across both commercial and defense aviation settings today.

Quantum joins the battlefield

Moroney’s comments came during a fireside chat titled “From clean fuels to safer flights: where is quantum advantage delivering real-world impact?” at Commercialising Quantum Global 2026, held in London and organized by Economist Enterprise.

The session was hosted by Laveena Iyer, senior analyst for telecoms and technology at the Economist Intelligence Unit. It explored how quantum computing and sensing are moving from laboratory promise toward deployed technology, a shift now visible across energy, defense and transport.

Q-CTRL’s ambitions extend well beyond navigation.

In May, the company said it achieved a 3,000-times speedup on a materials science problem using IBM’s quantum computers, cutting a simulation that once took more than a hundred hours down to about two minutes.

“Just three or four weeks ago we did it again, this time in the area of quantum computing,” Moroney said. “Using IBM’s computers, along with performance optimized using our software, we were able to solve a real-world problem faster than the best classical alternative.”

Q-CTRL followed that milestone with a white paper on quantum computing for defense, published in late May.

Titled “Quantum Computing for Battlefield Information Dominance,” it outlines four military case studies, including convoy routing, strategic airlift, defense manufacturing and missile defense, and the company said it used the approach to solve a simulated deployment of five thousand vehicles for the Australian Army during the military exercise Talisman Sabre.

The initiative also aligns with broader government efforts, including the United States’ National Quantum Initiative and the trilateral security partnership between the United States, Britain and Australia known as AUKUS, according to Q-CTRL.

Moroney pointed to poorly managed supply chains as a recurring problem in past conflicts, saying businesses and militaries alike have suffered when logistics were not planned with enough precision, and that better tools could help prevent similar delays and losses in the future.

He said quantum computers could join defense logistics workflows as soon as 2027 to optimize convoy routes and resource allocation.

“We were showing how quantum computing can solve exactly those types of problems to maximize the safety of all teams involved,” he said.

Beyond defense, Moroney named three areas where he expects quantum computing to deliver value soonest:

• Chemistry, including battery design and drug discovery

• Financial services, such as pricing assets against an uncertain future

• Optimization problems like supply chain management and scheduling for large organizations

“That demonstration we did a few weeks ago, of practical quantum advantage in computing, was on a problem that’s related to chemistry,” Moroney said. “We would be working with people who want to design new battery technology or drug discovery.”

On finance, he said quantum computing could help banks price assets whose value depends on an uncertain future.

“There are lots of enterprises who face very similar problems,” he said. “We work with a whole host of typically large organizations who have complex supply chains that they would like to manage or complex workflows that they would like to run optimally.”

The risk of waiting

Not every organization needs to act immediately, according to Moroney.

Moroney was asked what he would tell a board weighing an early investment in quantum technology against priorities such as cybersecurity and artificial intelligence.

“Frankly, it quite possibly is early to adopt quantum computing, because in the near term, quantum computers are going to be good at solving specific types of problems,” he said. “What I would then ask is: are you sure that’s not you?”

He said companies fall into three groups:

• dedicated teams already chasing quantum’s edge;

• fast followers trying to catch up;

• a large remaining group that has shown interest but not yet started exploring.

“We can’t say for certain that in the next three to five years you would be facing those benefits,” he said. “But if you’re not going through that exploration piece, then we know that you won’t be leveraging those benefits. It’s quite possible that your competitors will, and they will have the competitive advantage that goes with it.”

Moroney distinguished a proof of concept, which shows a computer can solve a problem, from proof of value.

“Quantum is hard,” he said. “It’s not that large of a word in letters, but I think it’s a very big word in impact, because it sounds so abstract and so difficult to grapple with.”

That complexity shows up in Q-CTRL’s work with Mazda, one of its enterprise clients.

“One of the things that Mazda has to do is run very complicated design software to understand how strong one of their beams is,” he said. “They don’t want to put too much material and make it very heavy and expensive, but nor do they want to sacrifice safety.”

Q-CTRL used a quantum computer to learn from a handful of data points as a cheap surrogate for costly runs. The team started with several candidate use cases before narrowing to the one judged most worth pursuing, rather than spreading its effort evenly across all of them.

Much of that work ultimately depends on internal literacy across the client’s own teams.

“We call it like Duolingo for quantum computing,” he said. “It’s an app-based educational software that allows interactive learning, and allows teams to get up to speed with this otherwise quite intimidating technology.”

Commercially, however, Q-CTRL is leaning on its sensing business first for near-term revenue.

“Our strategy is to use quantum sensing for revenue in the near term, and that will give us the buffer for the unknown timeframes to commercial adoption for quantum computing,” Moroney said.

With Ironstone Opal advancing and its defense white paper fresh, Q-CTRL’s next test is turning early demonstrations into lasting contracts, and Moroney said the company is now focused on finding those partners rather than chasing every possible use case at once.

A federal energy research agency in the United States is betting that quantum algorithms, not just quantum hardware, can help cut the cost of producing hydrogen fuel. The wager centers on catalysts, the materials that make industrial chemistry possible, and whether better ones can be found by simulating them on today’s imperfect quantum machines.

The project targets catalysts used in low-cost hydrogen production, with insights expected to extend to refining and metallurgy. Researchers say the difference between success and failure lies not in bigger quantum computers but in cleverer algorithms that extract useful answers from existing hardware.

Phasecraft, a quantum algorithms company with offices in the UK and the US, announced on June 15 that it had agreed to work with the Advanced Research Projects Agency for Energy (ARPA-E), alongside Johnson Matthey, Harvard and QuEra, on quantum algorithms for catalyst development aimed at reducing reliance on critical minerals for hydrogen fuel, among other things.

“This project, and others like it, are targeting something that is genuinely useful within the time frame of the project, rather than outputting a PDF with some numbers about what you might do one day,” said Ashley Montanaro, co-founder and chief executive of Phasecraft.

“The way that we think about algorithms at Phasecraft is that you can get gigantic reductions in computational costs by thinking abstractly across different hardware platforms,” Montanaro said. “We came up with a brilliant idea that can reduce the complexity of some problem by a factor of a million, for materials modeling, for example.”

Phasecraft designs software for today's imperfect hardware rather than future large-scale machines. It says its published work in materials simulation has delivered efficiency gains of up to 43 million times over earlier quantum methods.

Montanaro said this shift extends beyond the ARPA-E project, with end users increasingly approaching Phasecraft for outcomes rather than exploratory research. Projects now target results within about two years rather than open-ended timelines.

“End users are starting to realize that the sorts of collaborations they can set up with us are heading towards an actual useful outcome, something that’s going to make a difference to them,” Montanaro said. “The projects we’re thinking about now are ones which are targeting delivering something useful in the time frame of two years, and that’s a real difference in mindset.”

He said the sorts of collaborations he and others had some years ago would have been much more fact-finding missions, with end users trying to work out whether it would be 10 or 15 years before quantum computing affected their businesses.

Betting on noisy qubits

Montanaro’s comments came during a fireside chat titled “When the algorithms arrive: what really determines quantum advantage?” at Commercialising Quantum Global 2026, an event organized by Economist Enterprise in London.

Jason Palmer, host of The Economist’s “The Intelligence” podcast, moderated the discussion, which examined whether hardware progress or algorithm development is the bigger constraint on commercial quantum computing.

Montanaro co-founded Phasecraft and serves as its chief executive, leading a team that builds algorithms for chemistry, materials science and optimization problems designed to run on current quantum hardware.

He said the industry now has a broader set of hardware to build on, noting that the UK plans to procure 10 quantum hardware platforms in its first procurement phase.

He said Phasecraft was working with more and more quantum hardware companies, including Atom Computing, where it was developing and running algorithms on the company’s hardware platform.

Phasecraft and Atom Computing announced their own memorandum of understanding the same day, June 15, focused on adapting Phasecraft’s algorithms to Atom Computing’s neutral-atom hardware to accelerate the development of materials for batteries and solar cells.

Atom Computing has separately announced a $100 million letter of intent with the US Department of Commerce and is taking part in the Defense Advanced Research Projects Agency’s (DARPA) Quantum Benchmarking Initiative.

He said hardware companies increasingly see the benefit of working alongside software and algorithms specialists.

“There’s also more and more of a recognition from the hardware companies that they will benefit from working with the software and algorithms experts to get the most out of those hardware platforms,” he said.

Montanaro also returned to an assertion he made at the same event a year earlier, that near-term commercial value would come from running algorithms on today’s flawed hardware rather than waiting for fault-tolerant machines.

“I still absolutely believe that assertion,” he said. “We’re in this amazing time at the moment, where we’re at the dawn of the error-corrected and fault-tolerant era.”

“If you want to run the most high-performance experiments you possibly can and solve the biggest and hardest problems you possibly can, you’re better off using the noisy and imperfect qubits directly,” he said. “These machines are already showing their value for problems of scientific interest.”

He said problems of genuine commercial value remain on the horizon, with a two-year timeframe still realistic.

There is nothing inherently near-term or long-term about an algorithm, he added. The goal is simply to make it efficient enough, with few enough operations, to run on hardware available today or within the next year or two.

Selling algorithms, not promises

Montanaro said the industry increasingly recognizes the importance of algorithms, but investment has lagged behind that recognition.

“There is this very widespread recognition of the crucial role and importance of algorithms,” he said. “What needs to come now is people putting their money where their mouth is.”

“We’re beyond the stage of just having algorithms funded via research grants or very long-term things,” he said. “There are algorithms and software solutions that we have developed which can deliver scientific utility today. These are things that can now meaningfully be sold, where they couldn’t have been some years ago.”

The lag partly reflects assumptions about how quantum computing should develop. It is easy to assume hardware comes first and software follows, when algorithms can in fact be designed abstractly, ahead of any particular machine, and can achieve quantum advantage across multiple hardware platforms.

“If you’re a user and you have a particular problem you care about, the off-the-shelf quantum algorithms you see in textbooks probably are not going to be good enough,” Montanaro said. “If you want to get decent performance and do something genuinely useful, you’re going to need to work hard to tailor the algorithm to that particular problem.”

Hardware choice still matters. Superconducting qubit platforms tend to run fast but are more prone to errors, while ion trap platforms are slower but more reliable, meaning some problems suit one platform better than another.

“We’re not at a stage now where there is useful and meaningful quantum middleware that takes a problem and says it doesn’t matter what the underlying substrate is,” he said. “You should have an algorithm which really understands the hardware you’re working with and gets the most out of that hardware platform.”

He said most of quantum computing’s advantage comes from it being a fundamentally different computational model, which is where the sort of million-fold reductions Phasecraft has achieved originate. Applying that advantage usefully still means choosing carefully between platforms such as an ion trap or a neutral-atom device.

Looking five years ahead, Montanaro said the picture shifts again.

“In five years’ time it will be much more like a computational chemist will just use a quantum algorithm rather than a classical algorithm to solve their problems, because it’s just one of the standard techniques that happens to be the best one for a particular problem,” he said.

“The sorts of applications we expect to see in a couple of years’ time will be quite specific, quite targeted at particular problems which are really good fits for quantum computing,” he said. “In five years’ time, these domains will have expanded out of all proportion compared with where they are now.”

He added that cryptography is also likely to become an important application on that longer horizon, alongside the materials and chemistry problems Phasecraft is targeting today with ARPA-E and Atom Computing.

Quantum computing is starting to reshape aircraft design, not in theory but in early trials already delivering measurably better results.

The clearest gains so far sit in engineering simulation, where new algorithms are already producing answers that classical software cannot match, alongside early work on lighter materials, secure communications and precision navigation, areas engineers say are moving from theory into practice.

“A good example is aerodynamic modeling,” said Jasper Krauser, head of quantum technologies at Airbus. “If you want to design an aircraft, you rely on this capability. We are limited today by the computational power we have. Quantum computing can, in the future, make a difference, so you can design an aircraft that is potentially more fuel efficient and flies in a smarter way.”

“It took five times as long to run and needed five times as much memory, but it gave better answers,” said Leigh Lapworth, a fellow in computational science at Rolls-Royce. “Through most of my career, that’s what has mattered. Not necessarily faster, but better.”

Lapworth, who has spent nearly 40 years working in supercomputing at Rolls-Royce, said the code behind that result is nearly two decades in the making, only now reaching the end of what he calls phase one.

He said the work has been built and tested entirely in emulation, because no suitable quantum hardware existed to run it on. His team has published close to ten papers or preprints and maintains an open GitHub repository of test cases.

The group is now moving toward early trials on real hardware, though he said those machines remain too small to handle full computational fluid dynamics work.

Krauser said quantum’s usefulness at Airbus goes beyond raw computing power.

“We have use cases in quantum communication, for example secure communication infrastructure,” he said. “Quantum sensing is also an important topic for us, in terms of future improvements in navigation.”

He said the company is also pursuing early work on lighter, more resilient materials capable of withstanding harsh flight conditions.

“Quantum computers are particularly strong at simulation on a microscopic scale,” he said. “In future we can use a quantum computer to develop new types of materials, lighter materials, more resilient materials, because an aircraft needs to be ready to fly in very harsh environments.”

Lapworth said the underlying mathematics makes this possible. He said quantum mechanics is matrix mechanics, so any engineer who can do linear algebra can, in principle, write quantum algorithms.

Airbus is trying to widen that pool of use cases. Krauser said the company is running a joint quantum computing challenge with Volkswagen, HSBC, E.ON and Cleveland Clinic, inviting the wider community to propose applications, and is now waiting for submissions to come in.

Hybrid now proof pending

The comments came during a panel titled “What users are learning about use cases and platforms” at Commercialising Quantum Global 2026, organized by Economist Enterprise in London.

The session was moderated by Laveena Iyer, senior analyst for telecoms and technology at the Economist Intelligence Unit, who said that after three years moderating similar panels, conversations have shifted away from vague medium- to long-term timelines toward concrete, near-term deployment plans.

It examined how banks, logistics firms and pharmaceutical companies are testing quantum platforms differently.

Regev Yativ, chief revenue officer at Classiq, said finance and pharmaceutical clients are testing hybrid use cases, benchmarking them directly against classical methods to see where the impact is greatest. He said clients also want to know how the simulation work they are doing today will shape the business decisions they make in the future.

“Leading brands are implementing either a hybrid of classical and quantum, or using quantum as part of the tool set alongside AI and other technologies to create impact,” Yativ said.

Yudong Cao, co-founder and chief technology officer of Zapata Quantum, who has worked in quantum computing for 15 years, said the approaches vary widely. He said near-term heuristic algorithms already outperform classical methods in narrow cases, while fault-tolerant applications require far larger machines and quantum-inspired algorithms run on ordinary GPUs.

“Across the board, especially in finance, we’ve observed concrete cases where you can accelerate workflows with quantum-inspired, near-term quantum and fault-tolerant algorithms,” Cao said.

He said banks are often unwilling to reveal their own quantum work.

“A bank would hesitate to disclose its fraud detection or trading methods,” he said. “What we’re seeing behind closed doors is very different from what’s being published, and that’s worth paying attention to.”

The Rolls-Royce fellow said the bigger challenge is proving quantum systems are as reliable as the classical tools they replace.

“With classical code, you can run the same data hundreds of times and get the same answer,” he said. “With quantum, there’s no separation of data and compute. Every algorithm loads its own data, so different data means a different algorithm.”

Rolls-Royce’s own classical code has run unmodified in production for two years, executing roughly a million calculations a year.

“There are real difficulties in validation, verification and engineering processes,” he said. “We can only start those once we have the codes to run and test.”

Krauser said the same logic applies across the industry. Performance alone matters less than integration into the wider computing pipeline.

“When we talk about benchmarking, it’s not the quantum device itself; it’s always integrated. It’s the end-to-end performance that matters,” he said. “You might have a great advantage on the quantum part of an algorithm, but that gets lost if the hybridization isn’t done well.”

Racing to be ready

Away from the lab, panelists said the same urgency applies to strategy as it does to engineering.

“Quantum is not really a question of if. It’s a question of when,” Krauser said. “If you want to be prepared, it’s a great time to start right now, and there’s a great ecosystem there to help you get started.”

Yativ said the same logic applies to enterprise buyers weighing when to invest in a strategy.

“Hardware readiness could come tomorrow, next year or in two years,” he said. “We don’t know, but it could be tomorrow. If you’re not ready with the strategy, you don’t have the right use cases, the team or the organization. It’s late.”

Lapworth said the same discipline applies to collaboration. He said his four-person Rolls-Royce team has only reached its current position by working with outside partners, funded in part through the UK’s commercializing quantum program.

“There’s plenty of government money to collaborate,” he said. “Go forth and collaborate.”

Yativ said readiness also means confronting where data and code will live. He said the question comes up constantly as Classiq works with clients across different regulatory regimes and geographies, each with its own expectations about control and protection of intellectual property.

“Classiq works globally, and we hear about sovereignty every day,” he said. “People want their data safeguarded somewhere specific, and we have to take that into account, so we’re investing heavily in this.”

Cao said the same tension over control extends to intellectual property. He said the quantum community tends to focus on IP tied to the computational layer, while industry partners care more about domain-specific applications, and dividing IP along that line usually works for both sides.

“IP is a topic that can make or break successful partnerships,” he said. “The quantum community tends to care about IP that’s foundational on the computational layer, while industry domain experts are more concerned about specific vertical capabilities.”

Cao said fields like cryptography should stay within national boundaries, since the right approach depends on the application. He said the quantum community’s own conferences focus heavily on hardware and algorithms, adding that more industry voices are needed to define which problems actually matter and how to reformulate them for quantum systems.

Airbus and Rolls-Royce say the next test is turning today’s narrow gains into standard practice. Both expect quantum hardware to reach usable maturity within the next few years, leaving little time for competitors still deciding whether to start.

The quantum computers being built today still make roughly one error in every 1,000 operations. A computer trusted the way today’s ordinary machines are would need an error rate closer to one in a trillion trillion.

That gap is why executives want one checkable metric, real operations completed before failure, not loose terms like “logical qubits.”

“The metric I prefer can be immediately related to fault tolerance: the number of operations you’re running,” said David Rivas, chief technology officer of Rigetti.

He said that four years ago, people used the term to mean matching a classical computer’s reliability, an error rate of about one in a trillion trillion.

“The numbers we’re talking about now are more like one in 1,000, and that’s not a fault-tolerant computer,” he said.

He added that the term “logical qubits” has become loosely used over the past 18 months, following Google’s “Willow” error-correction paper, which drew a wave of investment into the sector and, he said, more noise than signal.

“Hype matters, and it’s on every vendor to make sure we don’t fall foul of it,” said Richard Murray, co-founder and chief executive of ORCA Computing. “If the public are skeptical, our customers are even more skeptical. We need to convince people with applications and benchmarking.”

Murray cited ORCA’s 25,000-variable optimization as evidence, and called the wider industry largely hype free.

Gillian Bussey, deputy chief science officer of the US Space Force, said senior leaders often overestimate quantum’s current abilities.

“I was in a war game once where a general said, ‘I just want quantum.’ We had to have a long conversation about what for, and the cost benefit,” Bussey said.

Yong Meng Sua, chief technology officer of Quantum Computing Inc (QCI), said benchmarking must translate into figures customers can act on.

“Benchmarking is ultimately the standard customers look to,” Sua said. “Beyond that, we have to translate benchmarking into business metrics that matter, so customers can put it into their budgeting and proposals.”

That shift matters increasingly for his own customers, he said, including NASA, one of the company’s clients.

The price of qubits

The discussion took place at Commercialising Quantum Global 2026, held in London and organized by Economist Enterprise. Tom Standage, deputy editor of The Economist, moderated the panel, which examined how far quantum computing has advanced over the past year and where the obstacles remain.

“The original business model for the company was to leverage the cloud,” Rivas said. “It boils down to how much of the quantum computer you’re actually using, and it’s not that expensive compared to supercomputers.”

“I suspect that in three to five years we’ll see predominant use of these machines over the cloud,” Rivas said.

He said the shift toward in-house hardware is driven partly by security concerns, and partly because the industry is still new enough that buyers want to experiment directly with the underlying technology themselves.

“One question we get often is: I’m already spending all the money I can on GPUs, why would I buy a quantum computer?” said Murray, noting that the economics have shifted since vendors could argue their systems were worth any price.

“We’re trying to make quantum relevant to commercial organizations by bringing the cost down to a few million dollars, so it makes sense alongside an investment in GPUs and other infrastructure,” he said.

He said the comparison increasingly comes down to a straightforward calculation: how much a given problem would cost to solve on an equivalent supercomputer, versus a quantum system, factoring in the cost of the hardware itself even when it is accessed over the cloud.

The panel also compared how competing hardware designs fare in a data center.

“We build hardware based on photonics that is data-center ready,” Murray said. “These systems are rack-mounted, mostly room temperature, and can be installed in a matter of hours, not days, weeks or years.”

He said Orca installed one such system for a Japanese customer that same week, taking six hours door-to-door.

“It’s neither complicated nor expensive technology that we’re dealing with,” Rivas said. “The data center we have in Fremont has a dilution refrigerator capable of cooling on the order of 100,000 physical qubits, and it costs less to run and takes up less space than a similarly sized rack of high-end AI chips.”

“People who’ve never worked with a cryo unit are afraid of them,” he added. “We’re not afraid of them.”

Dilution refrigeration is a well-established, 45-year-old technology supplied by multiple vendors, Rivas said. Rigetti builds most of its own technology from the ground up, not because it wants to be a full-stack company, but because it has to be.

The target price echoes the Pentagon’s own Quantum Benchmarking Initiative, which favors commercially relevant systems, Murray said. He said ORCA tries to leverage existing telecom infrastructure as much as possible to help hit that target.

Institutions such as the UK’s National Quantum Computing Centre are still swapping components to see what matters most, Rivas said.

Racing toward real applications

Quantum systems do not need to wait for full error correction to be useful, Murray said.

“We have the clear view that quantum can be useful before error correction,” he said. “We look at two general areas: accelerating generative AI, and optimization.”

“The mission of QCI is to put quantum into the hands of a billion people,” Sua said. “Beyond computing, we also offer secure communication, imaging and remote sensing.”

QCI has spent over a decade developing photonic chips built on thin-film lithium niobate (TFLN).

The platform lets the company build on two decades of existing silicon photonics manufacturing rather than reinvent the wheel, Sua said.

He cited a recent demonstration with networking company Ciena at the OFC optical networking conference as an example of fitting the technology into existing infrastructure rather than replacing it.

“We believe quantum communication will not replace existing communication,” he said. “We need to fit into the existing architecture, with a device similar to the form factor of the transceivers used today.”

Bussey said the CHIPS Act’s roughly $2 billion investment spans seven quantum computing companies, including Rigetti and Infleqtion.

“We’ve recently announced $100 million in investment in expanding our operations here,” Rivas said. “We’re also a recipient of some of the redeployed CHIPS Act money, about $100 million, to further our technical roadmap.”

“Retail and institutional investors jumped into the stock market. Government investment has exponentially increased,” Rivas said. “We have a billion dollars here, a billion dollars there.”

Bussey said the Space Force wants to be a smart consumer of quantum, not a builder. The Air Force Research Lab is also investing in nitrogen-vacancy (NV) diamond materials and supply chains, and already has access to Qiskit, the quantum software toolkit.

The first phase is algorithm work already underway at the Air Force Research Lab. The second brings optimization tasks to combatant commands within a few years. The third, a much longer push toward battlefield hardware, is constrained by weight and power.

She said her own preferences have shifted, starting with superconducting, which seemed the fastest path to a large-scale computer.

“Now I like neutral atoms, because you can do computing and sensing, which matters for the Space Force. I’m also interested in silicon spin, because the chips can be very small,” she said.

“It’s unlikely that the large data centers required to do AI are going to stick on a ship or show up in space,” Rivas said. “So you’re not going to stop using them, right?”

“Many modalities are going to be leveraged regardless of where they fit,” he said. “Deploying superconducting in space is utterly out of the question, but I think the data-center-in-space thing is going to happen. It’s not as crazy as people think.”

Asked what large organizations should do next, the panelists agreed that waiting is the bigger risk.

Bussey said companies should get onto the cloud now to build algorithms and use cases. Rivas said near-term machines are already useful enough to justify hiring staff who understand the technology.

Murray said the priority is mapping quantum’s first and widest applications, while Sua said the technology is moving faster than most procurement cycles can keep up with.