Advance Could Lead To Bigger, More Reliable Quantum Computers, University Of Edinburgh Scientists Say

TL;DR

Before you orderOffer from Amazon

Get the latest gadgets delivered free with Prime

• Fast, free delivery on millions of items
• Prime Video, Amazon Music and more included
• Member-only deals all year

Start your free Prime trial →Free trial for eligible customers · Cancel anytime

As an affiliate, we earn on qualifying purchases.Hide

Researchers at the University of Edinburgh and EPCC developed an algorithm that, in their tests, reduced the computing power needed for certain photonic quantum simulations to about 5% of the previous requirement. The work could make it easier to study and design light-based quantum computers, but larger-scale simulations and their practical impact remain to be demonstrated.

Researchers led by the University of Edinburgh and EPCC have developed an algorithm that they say lets standard computers run certain simulations of photonic quantum devices using about 5% of the computing power previously required. The result, reported in Science Advances, could make it less costly for researchers to study the limits of light-based quantum computers and test designs for larger, more reliable devices.

The algorithm was tested on Gaussian boson sampling, a computational task used to study photonic quantum systems. The team reports that, in its tests, classical computers running the new algorithm matched or exceeded the performance of some current quantum devices on the simulations. This comparison concerns performance on the specified task; it does not establish that ordinary computers outperform quantum computers generally.

The researchers say the method reduces the memory requirements, running time and energy costs of these simulations. The stated figure of about 5% of the computing power is the team’s comparison with the resources previously needed to perform the simulations using a supercomputer. The report does not provide a single universal reduction for every simulation or hardware setup.

The project involved researchers from Edinburgh and EPCC, the University’s supercomputing centre, as well as scientists at Polytechnique Montréal. The work received support from the UK Engineering and Physical Sciences Research Council and Canada’s Natural Sciences and Engineering Research Council. The team says it is now working to scale up use of the algorithm so it can support larger simulations.

Lowering the Cost of Device Testing

Quantum processors are still limited by their size and susceptibility to errors. Simulations on classical computers help researchers understand how a device may behave, identify limitations and compare possible designs. If those simulations require access to a small number of powerful supercomputers, the expense and availability of that infrastructure can constrain how often and how broadly researchers can run them.

By reducing the resources needed for a particular class of photonic simulations, the algorithm could make more testing feasible on commonly available computing systems. That could support research into larger light-based devices and help teams evaluate changes before building or testing hardware. These are potential benefits described by the researchers, not demonstrated outcomes from the reported tests.

The advance is about simulation, not a new quantum processor. It does not by itself increase the number of qubits or photons in a physical machine, correct hardware errors or show that a practical quantum computer is ready for use. Its value will depend on whether the method scales to more demanding simulations and gives useful results for real device development.

Why Photonic Simulations Matter

Photonic quantum computers use particles of light to represent and process information. Gaussian boson sampling is one task used to examine the capabilities of such systems. Researchers also run classical simulations of quantum circuits to compare expected behaviour with experiments and to investigate where current devices fall short.

These simulations can be computationally demanding, particularly as the systems being modelled grow. The source report says that previously, performing the relevant simulations required computing resources available at only a handful of facilities. The Edinburgh team’s approach targets that computational bottleneck by improving how the simulations use memory and processing resources.

The study is described as published in Science Advances, and the reported advance concerns classical simulation rather than a change to the underlying physics of quantum devices. The team includes academic and computing-centre researchers, with Canadian collaborators also involved. Its findings add a software and modelling development to a field where hardware remains small and error-prone.

““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.””

— Dr. Raul Garcia-Patron Sanchez, School of Informatics, University of Edinburgh

Scaling Beyond the Reported Tests

The report does not specify the full range of system sizes or hardware configurations tested, or provide enough detail to determine how the 5% figure would apply across different simulation tasks. It also does not establish that the algorithm can reproduce every relevant feature of larger photonic devices with the same resource savings.

The researchers say they are working to scale up the method, but the report gives no timetable or target size for that work. It remains unclear how readily the algorithm will be adopted outside the research teams, what computing resources larger simulations will require, and whether the resulting models will lead to measurable improvements in physical quantum hardware.

The comparison with current quantum devices is limited to performance on the Gaussian boson sampling simulations described by the team. It should not be read as evidence that classical computers are generally more capable than quantum computers, or that the wider promise of quantum computing has been tested.

Larger Simulations and Device Design

The team’s stated next step is to scale up the algorithm so that larger photonic quantum-computer simulations can be run using the same computing power, or potentially comparable resources. Further testing will be needed to establish how well the method performs as the simulated systems grow and whether its reported efficiency holds across different cases.

Researchers may then be able to use more detailed simulations to examine the behaviour of current devices and explore future designs. The source material does not identify a release schedule, a planned hardware demonstration or a specific follow-up milestone. The next concrete evidence to watch for is published work showing the algorithm applied to larger simulations and reporting its resource requirements and accuracy.

Key Questions

What did the Edinburgh-led team develop?

The team developed an algorithm for classical computers to simulate photonic quantum devices, including tasks involving Gaussian boson sampling.

What does the 5% figure mean?

According to the team, the simulations used about 5% of the computing power previously required to perform them with a supercomputer. The report does not say that this reduction applies to every simulation or setup.

Does the result mean standard computers are better than quantum computers?

No. The researchers report that classical computers matched or exceeded some current quantum devices on the specific simulations tested. That does not establish a general advantage over quantum computers.

How could the algorithm help quantum-computer research?

Less costly simulations could help researchers study device limitations and evaluate photonic quantum-computer designs. Whether that leads to larger or more reliable physical devices has not yet been shown.

What remains to be tested?

The team says it is working to scale the algorithm to larger simulations. Further results are needed to show how well it scales, what resources it requires and how useful the simulations are for designing real devices.

Source: rss