Quantum computing may already be solving business problems that classical machines struggle with, but users and developers still disagree on how close full-scale deployment is.
Developers say machines that exploit quantum effects without waiting for fault-tolerant hardware can already improve optimization. Corporate users see promising pilots but expect production-scale quantum computing to remain some years away. Consultants sit in between, reporting measurable gains on narrow problems while declining to call them a quantum advantage.
“It’s this misconception that you have to achieve, say, 10,000 qubits with gate fidelity of 99.99% for quantum computers to be useful that’s driving people away, while there is real utility in other forms of quantum computers that can help them now,” said Yuping Huang, chairman and chief executive of Quantum Computing Inc. (QCI). “We don’t have to wait for another day.”
“The technology matures, and it matures fast, but I don’t think we’re at the point where we can have a full-scale deployment right now,” said Asteris Apostolidis, senior lead for technical innovation at KLM Royal Dutch Airlines. “However, there are things we can do today.”
Huang said gate-based machines, first proposed to run Shor’s and Grover’s algorithms, still need years to become fault-tolerant.
“My definition is much broader. If you can utilize quantum effects to achieve certain advantages in processing data over classical means, that is quantum computing,” he said, citing QCI’s gate-free entropy quantum computing as an example.
Ricardo Collado, quantum lead in the office of the chief technology officer at EY Canada, said his team applies quantum computers to very pointed optimization problems in financial portfolios, supply chains and energy market networks, especially where adding a measure of risk makes classical modeling difficult.
“I would not necessarily call it a quantum advantage. That’s what we’re looking for at the moment. We’re just trying to show problems in which we can make a measurable difference, even though it might not be the academic definition of quantum advantage,” Collado said.
He said some problems, such as those involving higher moments or very complex functions, have no ready classical technique and would demand long research and development (R&D) work. A quantum formulation can shorten that path, and EY counts the speed from first idea to delivered solution as a key benefit.
Apostolidis said KLM monitors developments closely because, as a customer, it has no control over quantum computing's technical progress, while its own problems are pressing.
“Quantum computing is where the biggest value lies, but to have things in full production we still have some years to go,” he said. “In terms of maturity, quantum sensors are a bit more mature, at least in our domain of application.”
Testing on crew rosters
The panel took place at the Commercialising Quantum Insight Hour, an online webinar organized by Economist Impact on October 8. Moderated by Anjani Trivedi, global business correspondent at The Economist, the session examined whether quantum technologies are beginning to deliver something businesses will pay for.
Airlines must assign aircraft to routes, maintain them and roster cockpit and cabin crews. Apostolidis said carriers tackle this with heuristics that split the task into smaller pieces and settle for suboptimal answers.
“If you strip an airline down to its fundamental elements, what you see is that an airline is in principle a huge scheduling and optimization problem,” he said. “That might sound simple, but in reality it’s a very complex scheduling and optimization problem, and whoever cracks these kinds of problems first will have a tremendous advantage.”
KLM recently ran a crew-scheduling trial with the Netherlands Organisation for Applied Scientific Research (TNO). Rosters must match the fleet, network and flights to the aircraft types and the days each pilot and cabin crew member can fly, while respecting collective labor agreements and personal circumstances.
“That was our starting point, seeing whether quantum can accelerate things, and I can tell you the results were very promising for this part of the overall problem,” Apostolidis said.
He said the wider value lies in network and maintenance planning. Airlines run very expensive assets on thin profit margins, so every efficiency gain counts, and faster recalculation would help them respond to disruptions such as weather-related cancellations. He said this applies to all carriers, not just KLM.
Huang said QCI’s cloud users have applied its machines to crew deployment and scheduling, including for some potential space missions.
Collado said today’s quantum machines cannot ingest the volume of data a classical data center can, so EY has developed its own decomposition methods to break huge optimization problems into pieces small enough to solve.
EY targets cases where a small gain carries big value, such as a 10% lift in portfolio returns.
“Always compare against a meaningful classical baseline,” he said. “It’s not all about solving computations faster or solving bigger problems. There are other metrics we can look at that we might not be able to capture with classical methodologies.”
Quantum machine learning typically lifts classification metrics such as F1 score and standard accuracy by 3% to 8%. He said EY’s models showed similar gains, and their solution geometry kept them near-optimal far longer than classical ones, so clients would need to retrain less.
Apostolidis said even a small gain over KLM’s baseline prompts the team to work out how to maximize it.
“We celebrate our wins and our failures equally, because by failing to prove that something works in a quantum way, you know what not to pursue in the future,” he said.
Q-Day moves closer
Collado said that three or four years ago, the arrival of quantum computers strong enough to break today’s encryption, known as Q-Day, was expected in 10 to 15 years. Recent improvements from major vendors and new research have pulled that timeline forward.
“Now we believe Q-Day will happen anywhere from two to five or six years from now. It’s not so far in the future,” he said.
He said the shift has created urgency for EY’s quantum security work, which helps clients move to quantum-resistant encryption. He credited hard science and engineering at big companies, rather than artificial intelligence (AI), with driving the urgency, since AI's role in quantum computing remains nascent, citing more bullish vendor roadmaps and recent advances in error correction.
Huang called post-quantum cryptography (PQC) a great initiative but said new attack types will always emerge.
“The only way to ultimately secure the internet is to tie it to something physical,” he said. “As long as quantum physics is not wrong, your internet will be secure.”
Apostolidis said quantum sensing could geolocate aircraft and other equipment in the air without the global positioning system (GPS).
“In the current geopolitical state of the world, we see lots of jamming and spoofing of aircraft positioning, so that’s a very fascinating possibility,” he said.
KLM is also part of a Dutch research program with universities and other partners exploring quantum sensors for health monitoring of aircraft systems, which Apostolidis said might become a first application. He said the airline also handles very sensitive passenger and technical data, where quantum communications could help.
Huang said quantum sensors should push detection distance, precision and accuracy beyond what classical techniques can reach.
Apostolidis said he first explored quantum for aviation in 2018, when he was usually the only company representative in a room full of mathematicians and physicists. Rooms are now full of people with a business interest, and KLM’s executive management is asking questions. He said colleagues run internal webinars and department visits to raise awareness.
“As the technology matures, our internal organization matures at the same time, so we don’t waste time waiting for the right moment, but make sure we’re ready when the technology is ready for us,” he said.
Collado named talent and hardware as today's biggest obstacles.
“We need more people to be able to work with these solutions, and it’s not as easy as it is in machine learning to get new people into the field,” he said.
Huang said quantum could help AI infrastructure grow without the energy cost of stacking thousands of graphics processing units (GPUs).
To cut the price tag, QCI is pursuing room-temperature, chip-integrated hardware that avoids cryogenic overheads and could sit on the same motherboard as central processing units (CPUs) and GPUs.
Apostolidis said no company can address these problems alone, and KLM is forming consortia to explore its quantum options.