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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsA new algorithm from researchers at the University of Edinburgh, the Edinburgh Parallel Computing Centre (EPCC) and Polytechnique Montréal allows ordinary, off-the-shelf computers to emulate a demanding photonic-quantum workload using about 5% of the computing power previously required. The method targets Gaussian boson sampling, a specialised benchmark used to study photonic quantum computers. It is a more efficient simulator—not a quantum processor and not a replacement for general-purpose quantum hardware.
What the Edinburgh team developed
Tom Dodd and colleagues describe the method in A fast and frugal Gaussian Boson Sampling emulator, published in Science Advances in 2026 (DOI: 10.1126/sciadv.aef8431). The work is an algorithmic emulator: software that reproduces the output of a particular quantum-computing process on classical hardware.
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The reported advance cuts memory use, running time and energy requirements for the target simulations. The University of Edinburgh says the new approach needs around 5% of the computing power previously needed for comparable work, potentially moving experiments that were limited to a handful of major supercomputing facilities onto more widely available systems.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →That percentage describes the computing requirement reported for this simulation advance. It is not a claim that every quantum algorithm, or every quantum-computing simulation, now runs on a typical desktop at 5% of its former cost.
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What Gaussian boson sampling means
Gaussian boson sampling (GBS) is a sampling problem built around bosons— in this case, particles of light—passing through an optical network. A photonic quantum computer prepares light in particular states, sends it through a circuit of beam splitters and phase-sensitive components, and measures the pattern of photons at the output.
The resulting probability distribution is difficult to calculate as the number of optical modes and detected photons increases. Because the setup is well defined, researchers can use GBS as a benchmark: they can compare a quantum device’s measured samples with a trusted classical calculation and investigate where noise, loss and limited scale affect the result.
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How the new emulator works in practice
The key contribution is a more economical way to calculate the probability distribution produced by a GBS circuit. Earlier classical approaches combined sophisticated mathematics with large memory allocations and extensive parallel processing. The Edinburgh-led method reorganises that calculation to avoid much of the repeated work and reduce the amount of intermediate information that must be stored.
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The paper’s result is therefore best understood as a software and algorithm improvement. The computer remains classical: it uses conventional processors and memory, while the program mathematically models the behaviour of a photonic quantum circuit.
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What the tests showed
In the reported tests, classical machines running the emulator matched and, for the tested workload, outperformed some current quantum devices. “Outperformed” here refers to the ability to generate or analyse the relevant GBS simulation results under the comparison conditions; it does not establish that classical computers outperform quantum computers on arbitrary tasks.
The comparison is meaningful because quantum processors available today are still relatively small and error-prone. A fast classical reference lets researchers determine whether an observed output reflects useful quantum behaviour, ordinary statistical variation or hardware errors.
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Why cheaper simulation matters
More researchers can run experiments
When a simulation requires a national-scale supercomputer, access is constrained by queue times, allocation rules and specialist staff. Lower requirements could let university groups and smaller laboratories test circuit designs, error models and analysis methods on local clusters or other standard computing resources.
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Photonic quantum systems need larger circuits, lower loss and more reliable measurements. Classical emulation supplies a reference as those systems are redesigned. Researchers can test a proposed change in software before committing time and money to an optical experiment.
Energy and operational costs fall
Reducing runtime and memory traffic also reduces the energy consumed by a simulation. The Edinburgh statement specifically identifies memory use, running time and energy costs as areas of improvement, although it does not publish a universal energy figure for every computer or circuit size.
What this does—and does not—replace
| Approach | What it provides | Main limitation |
|---|---|---|
| New classical GBS emulator | Software emulation of a defined photonic sampling workload, with a reported requirement of about 5% of the previous computing power | Scope is the GBS workload; it is not a general quantum processor |
| Direct photonic quantum hardware | Physical generation and measurement of photons in an optical circuit | Current devices are small, noisy and difficult to validate without classical references |
| Earlier large-scale classical simulation | Reference results for quantum circuits using established algorithms and supercomputers | Greater memory, runtime, energy and facility requirements than the new method, according to the reported comparison |
The emulator cannot create quantum entanglement in hardware, provide a speedup for unrelated algorithms or remove the need to build and improve quantum processors. It makes one important research workload easier to study.
Can a normal computer simulate a quantum computer?
Yes, but only within limits. A normal computer can simulate selected circuits, models or sampling tasks, and the new work shows that a particularly demanding photonic workload can be made substantially cheaper. Simulation cost still grows with problem size, and the report does not give a single desktop specification, maximum circuit size or independent replication table that would support a universal “runs on any laptop” claim.
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For someone learning quantum simulation, an open-source platform such as PennyLane can provide circuit-building and simulator tools across quantum-computing and quantum-machine-learning workflows. A software framework is a way to experiment with models; it is not evidence that every large-scale simulation will fit on consumer hardware.
What researchers plan next
The team says it is working toward larger simulations using the same computing resources. The practical test will be whether the algorithm retains its savings as the number of optical modes, photons and circuit operations increases. Larger cases will also test how accurately the emulator represents imperfections such as loss and detector noise.
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Important limitations when interpreting the announcement
- The 5% figure is a reported comparison for the described simulation advance, not a price, runtime or energy guarantee for every setup.
- No complete hardware configuration is provided in the report summary, so processor model, memory capacity and exact wall-clock times cannot be inferred.
- The reported comparisons concern Gaussian boson sampling and some current quantum devices, not the overall performance of classical computing against quantum computing.
- Independent replication details and a full cross-platform benchmark table are not established in the available announcement.
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