Credit: Phasecraft
Phasecraft has produced what it says is the largest known database of variational quantum eigensolver results, more than 3,000 emulations across 13 molecular systems.
The work ran on Nvidia Hopper processors hosted at the University of Nottingham, using Nvidia’s cuQuantum toolkit, and the company reports a 15-fold speedup over earlier results for modelling many-body systems.
The emulations covered circuits of four to 32 qubits, concentrated in the 24-to-28 range. That detail is the one to hold on to, because it places the whole exercise inside the territory where classical computers can still simulate quantum ones.
No quantum hardware was used. Phasecraft built a picture of what a quantum computer would calculate, on machines that are not quantum computers, which is currently the only way to generate data at this volume.
Doing it this way is reasonable, and the company is not hiding it. Quantum chemistry is the application most often promised for quantum computing, and useful hardware does not yet exist.
Building the training data now, so that quantum-enhanced density functional theory has something to work from when machines arrive, is a defensible strategy rather than a workaround.
Phasecraft cites no paper and no preprint for the 15x figure, and does not name the benchmark it improved on beyond “prior results” for many-body systems.
Compare that with its 10x claim in March last year for the THRIFT algorithm, which landed in Nature Communications on the day it was announced. This one arrives as a company post co-signed by the vendor whose chips it credits.
Ashley Montanaro, the Bristol company’s chief executive, said that making good on quantum computing’s promise means pushing the limits of today’s most capable hardware.
Sam Stanwyck of Nvidia said the work shows what happens when leading researchers get access to accelerated computing, which is the sentence a chipmaker writes.
Montanaro has stood on the other side of this before. In September 2024, he told TNW that Britain had a chance to become home to the next quantum Nvidia, and argued for stable funding and lighter export controls to get there.
Two years on, his company’s largest result is announced jointly with the actual Nvidia, on Nvidia silicon, using Nvidia software. None of which is hypocrisy, but it is a fair description of where value in quantum computing is accruing while the hardware matures.
The database approach is consistent for Phasecraft. In January 2024, it published a materials complexity database covering more than 40 materials alongside a new algorithm. The pattern is the same: produce the reference data first, and be positioned when hardware catches up.
Funding comes through Wellcome Leap’s Q4Bio programme, which asks whether new algorithms can deliver quantum advantage in health. The stated destination is drug discovery, though nothing in the announcement describes a molecule found, a compound screened or a result a pharmaceutical company could act on.
The claim is that the dataset gives quantum-enhanced molecular modelling something to train on.
For scale, Britain’s second commercial quantum computer, a Rigetti machine that came online in April 2024, runs 32 qubits. Phasecraft’s emulations reach the same number on classical hardware. The gap that matters is not qubit count but error rates, and closing it is somebody else’s problem.
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