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A 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 demanding Gaussian boson-sampling experiments using about 5% of the computing power previously required. The result is a cheaper way to study photonic quantum-computing devices—not a replacement for a quantum processor.
What the researchers developed
Tom Dodd and colleagues describe their method as a “fast and frugal Gaussian Boson Sampling emulator” in a 2026 Science Advances paper (DOI 10.1126/sciadv.aef8431). It is an algorithmic improvement for classical simulation. Instead of building a new quantum chip, the team reduced the memory, running-time and energy demands involved in reproducing a particular photonic-quantum workload on classical machines.
The University of Edinburgh’s 2026 account says the new approach uses around 5% of the computing power previously needed for the same class of simulations. That figure is a reported comparison from the team; the report does not provide a universal hardware specification or an independent replication table, so it should not be interpreted as a fixed saving for every quantum simulation.
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A photonic benchmark
Gaussian boson sampling (GBS) is a benchmark-style problem associated with photonic quantum computers. These systems encode information in particles of light and use optical components to generate and measure complex probability distributions. GBS is useful for testing whether a device can produce results that are difficult to calculate by conventional methods as the system grows.
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What an emulator does
An emulator calculates the expected behavior of that photonic circuit on a classical computer. It can help researchers check experimental results, investigate noise and imperfections, and explore how a proposed device might scale. The emulator reproduces the chosen workload; it does not create quantum states or provide the broad capabilities of a programmable quantum computer.
Why the 5% resource requirement matters
Large quantum simulations have traditionally depended on a small number of major supercomputing facilities. Their cost is not limited to processor time: memory capacity, elapsed runtime and electricity consumption can all become barriers. Reducing those requirements can let more research groups run meaningful experiments, repeat results more often and test larger instances without waiting for scarce supercomputer allocations.
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That accessibility is particularly important because current quantum devices remain relatively small and error-prone. Classical simulation gives researchers a reference point for identifying hardware errors and for designing larger, more reliable photonic systems. The Edinburgh team says it is working toward larger simulations using the same computing resources.
What the tests reportedly showed
In the reported tests, classical computers running the new algorithm matched and, for this workload, outperformed some current quantum devices. “Outperformed” here refers to completing or reproducing the GBS simulation task under the comparison used by the researchers. It does not establish that classical computers are generally faster than quantum computers, nor that the method solves every quantum-computing problem.
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Does this replace a quantum computer?
No. The advance changes the cost of simulating one important photonic workload. A classical emulator still has to calculate the result using conventional processors and memory, while a quantum processor performs operations through physical quantum states. The method therefore supports quantum-hardware development rather than making quantum hardware unnecessary.
Its scope also matters. Results for GBS cannot be transferred automatically to quantum chemistry, optimization, error correction or arbitrary quantum circuits. As problem sizes increase, classical simulation can again become impractical; the team’s stated goal is to push that boundary further, not to remove it.
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How it compares with direct hardware and other simulators
| Criterion | New GBS emulator | Direct photonic quantum hardware | Other classical simulators |
|---|---|---|---|
| Simulation scope | Gaussian boson sampling | Physical photonic circuits and their measurements | Depends on the algorithm and circuit model |
| Required hardware | Off-the-shelf classical computers, according to the report | A photonic quantum device and associated optical and measurement systems | Usually classical computing hardware; exact requirements vary |
| Memory and runtime | Reported at about 5% of the previous requirement for the tested simulations | Determined by the device and experiment rather than classical memory | Not stated for a common baseline |
| Energy cost | Reportedly reduced alongside memory and runtime | Includes operating and laboratory infrastructure | Varies by implementation and hardware |
| Fidelity to a target photonic device | Designed to emulate the GBS workload | Direct physical behavior, including real device imperfections | Depends on the model and noise assumptions |
| Scaling | The team is pursuing larger simulations with the same resources | Limited by the size, error rate and engineering of the device | Varies; no general scaling result is established here |
What this means for students and developers
The work makes experimentation more accessible, but it does not imply that a particular desktop application is automatically available. The published report identifies the algorithmic advance rather than specifying a consumer download, operating-system package or minimum computer configuration.
Readers who want to learn quantum simulation can start with an open-source platform such as PennyLane, which provides tools for quantum computing, quantum machine learning and quantum chemistry and documents integrations with several hardware and software providers. A general framework can teach circuit construction and simulation concepts, but it should not be assumed to implement Dodd and colleagues’ emulator unless the project documentation explicitly says so.
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Limits readers should keep in mind
- The 5% figure is an approximate reported requirement for the relevant simulations, not a guaranteed reduction for all circuits or computers.
- The available report does not state a complete hardware configuration, runtime table or independent replication results.
- Matching or beating some quantum devices on GBS simulation says nothing about universal quantum advantage.
- Classical simulation remains constrained by problem size, memory and algorithmic complexity.
What happens next
The immediate opportunity is broader testing of photonic quantum designs: researchers can vary circuit parameters, model imperfections and compare simulated distributions with laboratory measurements at lower cost. If the approach continues to scale, experiments that once required specialized supercomputers could become practical on more widely available systems. That would accelerate evaluation of quantum hardware while leaving open the central question of where physical quantum processors will eventually outperform classical simulation.
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