UK quantum computing firm gets $4.5m for hydrogen project

Phasecraft, a specialist in quantum algorithms, has received a $4.5 million contract with the US Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E) to advance quantum computing approaches to simulate and discover novel catalysts for use in the energy sector.

The project is aimed to reduce the current reliance on critical minerals, in particular platinum group metals such as iridium, that are used in catalysis.

The initial focus is on low cost hydrogen production, with insights expected to apply across syngas production, petroleum refining, metallurgy and other industrial sectors whose economics depend on the chemistry of catalysts.

To complete the project, Phasecraft will partner with Johnson Matthey, Harvard and the Boston-based quantum computing company QuEra.

"Quantum computing is no longer a distant promise. It’s a working technology, and the question now is which problems it gets pointed at first,” said Ashley Montanaro, co-founder and CEO of Phasecraft.

“As industry and governments work together to realise the full promise of quantum computing, we are grateful that ARPA-E has chosen Phasecraft to help solve this critical set of problems on a meaningful timescale.”

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According to a statement the approach builds on Phasecraft's published work in quantum materials simulation, where tits algorithms have achieved efficiency improvements of up to 43,000,000× over previous quantum methods.

Steve Flammia, Principal Quantum Scientist and head of Phasecraft US, adds that hardware-adaptive quantum algorithms hold immense promise for priority problem sets.

“Cutting the iridium requirement in industrial electrolysis would meaningfully change the economics of hydrogen fuel and a wider class of catalytic processes that underpin energy security. Delivering significant quantum speed ups with hardware-adaptive algorithms could help shape iridium requirements in a matter of years, not decades.”

The ARPA-E QC3 aims to accelerate energy innovation by supporting the development and application of quantum computing approaches to chemistry and materials science problems that lie beyond the reach of classical computers.

AI and quantum

The development of new materials is one of the key use cases for quantum computers due to the increase in speed that it offers but more significantly because the molecules that make up materials are governed by quantum physics.

Recent research at the University of Washington has shown how AI can complement quantum computing to further accelerate new material design.

In the project, the researchers have shown how AI can be used to simulate dozens of sheets of atoms stacked in intricate patterns – a process that produces complex and potentially useful quantum behaviours.

In a second study, they have shown how quantum computers can create a self improving design loop by discovering new materials that could themselves be components of future quantum computers.

Equally AI can form an integral part of quantum computing and for example Nvidia has recently released the Ising open source quantum AI models for researchers and enterprises to build more advanced quantum processors.

These are asserted to further enable quantum computation by delivering up to 2.5x faster performance and 3x higher accuracy for the decoding process needed for quantum error correction.

This is crucial for what will see growing application. The Quantum Economic Development Consortium’s state of the industry report identifies over 7,400 quantum engaged organisations worldwide at the end of 2025, including over 550 pure play quantum companies, and projects the market to grow at a rate of 32% annually in the years ahead.

Energy efficiency

An issue of growing concern on the back of the focus on data centre energy consumption is that of the increased use of quantum computing and more advanced quantum computers.

Given the still relatively nascent status of quantum computing future energy consumptions are hard to estimate. A recent analysis from Oak Ridge National Laboratory suggests that by the 2040s the power demand of quantum data centres could be similar to that of today's AI data centres.

However, they also identify possible scale up bottlenecks due to the needs for certain physical resources, in particular water and helium-3.

Much depends on the efficiency gains that can be made. For example, current technology becomes difficult when scaling beyond 1,000 qubits and impossible if the goal is one million qubits. With current technology, the energy consumption of such a quantum computer would be comparable to the output of an entire nuclear reactor.

To address this the QScale project is being launched under the coordination of VTT in Finland with Tampere and Aalto Universities as partners.

The aim is to achieve a radical improvement in the energy efficiency and scalability of quantum computers through optical communications control and ultra-precise superconducting signal technology.

The optical telecommunications technology also could help seamlessly connect the computing capacities of quantum and supercomputers, which in turn is expected to make AI computing more efficient.

The aim is to commercialise the QScale technology in the 2030s, first for controlling superconducting quantum computers and later for numerous other applications.