Phasecraft’s Algorithmic Innovations Speed Path to Quantum Advantage
Advances in quantum algorithms are bringing scientific breakthroughs closer, enabling applications from battery innovation to energy system optimisation
Quantum advantage — the point when quantum computers outperform classical machines on meaningful scientific or industrial tasks — could be far closer than many expect. While much attention focuses on improving hardware, one UK quantum specialist argues that advances in software may be the decisive factor in accelerating that timeline.
Phasecraft, a British quantum algorithms company, was founded by a team of quantum scientists and engineers developing methods to make practical use of today’s quantum devices. Their work focuses on problems where quantum methods can deliver a measurable edge over classical approaches — including simulating complex materials for next-generation batteries, improving catalysts for clean energy, and optimising the performance of renewable energy systems.
Ashley Montanaro, Phasecraft’s co-founder and chief executive, views algorithms — the intellectual core of computing — as the driving force behind this acceleration.
“In classical computing, the advantages from better algorithms over the last 30 or 40 years far outstrip the advances coming from hardware performance,” he said. “We’ve seen the same thing in the quantum space as well — developing better algorithms can reduce the cost of solving problems by factors of tens of millions or more.”
This approach challenges the notion that major breakthroughs must await the development of large-scale, fault-tolerant quantum computers. By designing more efficient algorithms, researchers can enable existing hardware to deliver valuable results now, thereby narrowing the gap between current performance and the long-promised potential of the technology.
Montanaro noted that by refining algorithms to operate within the strict time limits imposed by today’s quantum devices — which can only run calculations for short durations before noise overwhelms results — his team has already matched some of the best classical methods. With further refinement, he believes genuine “better than classical” results are imminent.
He predicts a significant milestone in the near term: “Within a year from now, we’re going to see something which is a scientific quantum advantage milestone being met, which is quantum computing outperforming classical computing for solving a genuinely scientifically meaningful problem.”
He urged industry leaders to act early rather than wait until after the breakthrough. “If you want to be a leader in this space, the time to jump in is not six months after — it’s six months or a year before. So now.”
Material simulation and energy optimisation
Speaking in London at Commercializing Quantum Global 2025, Montanaro explained how Phasecraft designs platform-agnostic algorithms that can be adapted to different quantum architectures. While the underlying physics can often be applied across systems, optimising performance requires tailoring to the specific capabilities and constraints of each hardware platform.
Phasecraft’s most active research areas involve simulating materials at the quantum level — work that is essential for designing high-performance batteries, solar cells, and catalysts.
For example, accurately modelling the behaviour of electrons in complex chemical systems is exceptionally challenging for classical computers but a natural fit for quantum machines. The company develops algorithms to represent these systems on qubits and extract proper measurements within the short “coherence window” available before errors set in.
Other projects focus on optimising energy systems, such as improving grid efficiency and planning renewable energy generation. Here, Phasecraft’s algorithms aim to solve complex mathematical optimisation problems faster or more precisely than classical methods can achieve.
In practice, the company’s hybrid approach blends quantum algorithms into established classical computing workflows. Quantum processors excel at tasks where they offer a clear advantage — such as modeling quantum correlations in materials — while conventional high-performance computing handles everything else. This integration allows clients to run proof-of-concept projects now, extracting value from today’s noisy, small-scale machines without waiting for future hardware.
Partnerships, patents, and industry needs
Phasecraft collaborates with industrial partners in fields where better materials and improved system design can create a competitive advantage.
Montanaro mentioned working with battery developers, renewable energy companies, and chemical manufacturers to explore specific use cases. Publicly, the company has cited collaborations with organisations such as Johnson Matthey in battery materials and Oxford PV in solar technology.
While quantum computing’s academic heritage has fostered a culture of collaboration, Montanaro acknowledged that commercialisation is bringing change.
Phasecraft patents some of its techniques and may license them broadly, while in other cases, it offers exclusivity to strategic partners.
“There are some who want to be the only one in their sector that is getting the quantum advantage,” he said. “Others want leading technology but don’t mind if another organisation has it as well.”
This variety of approaches reflects the early stage of the quantum industry, where no single commercial model has yet become dominant.
Despite increased interest, Montanaro still encounters misconceptions that slow adoption. A persistent myth is that quantum computers run existing classical algorithms faster.
“You can’t run your classical algorithm on the quantum computer and make it run faster,” he said. “In general, quantum computers are physically slower than classical computers. You need to come up with a new algorithm that takes advantage of quantum mechanics.”
Another misconception is that quantum systems are suited to “big data” problems. Montanaro explained that the opposite is true: their most promising near-term uses are in simulating quantum physical systems — from new battery materials and catalysts to superconductors — and solving tough optimisation challenges.
From incremental improvements to paradigm shifts
While refining existing algorithms is critical to reaching the first quantum advantage milestones, Montanaro believes entirely new classes of algorithms remain to be discovered. He compared this to the early days of classical computing, when breakthrough algorithms suddenly expanded the scope of what was computationally possible.
He pointed to Google’s recent Decoded Quantum Interferometry (DQI) algorithm as an example of such fresh thinking.
DQI tackles specific optimisation problems by converting them into classical decoding tasks, which can then be solved using powerful existing methods. In benchmark tests, it has outperformed some general-purpose classical optimization techniques, such as simulated annealing, and in specific cases has demonstrated the potential for an exponential quantum speedup.
Alongside such developments, Phasecraft has created new methods for combining quantum computing with density functional theory — a widely used approach in materials modeling — to make simulations more efficient. This hybrid approach could yield new insights much sooner than purely quantum or purely classical methods alone.
Montanaro believes more transformative algorithms will follow as hardware improves. “It would be unimaginable to me that we have this amazing new technology and there’s nothing we can do with it beyond the things we figured out before we even got our hands on advantage-level versions of this technology,” he said.
For businesses, the implications are clear. Quantum advantage may be less than a year away in specific domains, and developing meaningful projects takes time. Building internal expertise, identifying target applications, and partnering with quantum specialists now could position companies to capture first-mover benefits.
Phasecraft’s focus on material simulation and optimisation offers a clear example of how quantum algorithms can be applied to tangible, high-value problems. If Montanaro’s prediction holds, the competitive landscape in energy, chemicals, and advanced manufacturing could shift rapidly — driven not just by faster hardware, but by more innovative software.