Quantum Algorithms, Quantum Simulation

Canada and UK fund software for better quantum devices

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October 15, 2026 by Ivy Delaney

Classical computers, running a newly developed algorithm, can now match and even exceed the simulation capabilities of some existing quantum devices. It means the simulations can be run using around five percent of the computing power previously needed to perform them with a supercomputer, the team says. This advance promises to lower the cost and accelerate research into building more reliable quantum computers, particularly those using light-based technology. Raul Garcia-Patron Sanchez of the School of Informatics.

Algorithm Reduces Simulation Power for Photonic Quantum Computers

The new algorithm enabled classical computers to match the simulation performance of some existing quantum devices, a surprising result given the expectation that quantum hardware would inherently outperform classical systems in these tasks. This shift in capability has implications for the development of photonic quantum computers, a specific architecture benefiting from the ability to run more detailed simulations.

The efficiency gains are substantial; the team reports simulations now require approximately five percent of the computing power previously needed. This reduction stems from improvements in memory use, running time and energy costs within the simulation process itself, making detailed modeling accessible to a wider range of research facilities.

Researchers are currently focused on scaling up the algorithm’s application, with the goal of running even larger simulations using the same computational resources. This expansion promises to unlock greater detail in the modeling of photonic quantum computers, accelerating the design and refinement of these complex systems and potentially informing the development of more robust quantum technologies.

Classical simulation of large-scale quantum computing circuits has been achieved so far via a combination of clever algorithms and the intensive use of supercomputing facilities. Our work has contributed significant improvements in memory use, running time and energy costs in the simulation of photonic quantum computing devices, making mainstream what was before only accessible to a handful of facilities in the world.

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Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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