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The most reliable first experiment is a two-mode, non-Gaussian circuit: prepare the Fock state |1,0⟩, mix the modes with a beamsplitter, and measure photon numbers. Run it locally first, increase the Fock cutoff until your result converges, then repeat with a fixed shot budget and a documented reference. This separates three effects that are often confused: numerical truncation, statistical sampling, and actual hardware behavior.
What your first photonic circuit does
A photonic quantum circuit operates on optical modes rather than qubit wires. In the Fock representation, each mode is described by an occupation number: 0 photons, 1 photon, 2 photons, and so on. Gates such as beamsplitters and phase shifters transform those mode states, while measurements return photon-number outcomes.
A useful first circuit has two modes and one photon initially in the first mode. A beamsplitter redistributes the photon between the modes, so the measured outcomes are easy to interpret while still exercising a non-Gaussian Fock-state simulation. PennyLane’s official hybrid-computation example uses this pattern and requires the Strawberry Fields Fock backend for the non-Gaussian state.
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Choose the simulation route
| Route | Best for | State and measurement model | Important control |
|---|---|---|---|
| Native Strawberry Fields Fock engine | Learning the native circuit model and obtaining occupation-number samples | Fock-state simulation with Fock measurements | Engine choice, cutoff dimension, shots |
PennyLane strawberryfields.fock |
Hybrid quantum-classical circuits, gradients, and PennyLane workflows | Full continuous-variable Fock simulation | Wire count, cutoff_dim, analytic versus finite shots |
PennyLane strawberryfields.gaussian |
Circuits restricted to Gaussian operations | Gaussian operations with photon-number-resolving measurements | Gaussian-device restrictions |
PennyLane strawberryfields.gbs |
Gaussian boson sampling and graph-embedding experiments | GBS probabilities and samples | Graph embedding, cutoff, shots, optional sample caching |
PennyLane strawberryfields.remote or a Strawberry Fields remote backend |
Cloud or photonic-hardware behavior | Execution on a remote service rather than only local simulation | Backend, queue, access conditions, and service date |
For a first non-Gaussian experiment, use the Fock route. The Gaussian device is not a substitute for a Fock-state experiment, and a GBS device is designed for a different sampling problem.
Prerequisites and a reproducible setup
Install a current Python environment, Strawberry Fields, and, if you want the PennyLane interface, PennyLane plus its Strawberry Fields plugin. Check the APIs exposed by the versions actually installed: gate names, engine arguments, device options, and measurement support can change between releases.
The PennyLane documentation identifies plugin release 0.29.1 and states compatibility with PennyLane versions up to and including 0.29. Treat that as a compatibility note for the documented release, not as a guarantee for every newer environment. Record the exact package versions in your benchmark.
- Record the Python version and operating system.
- Record Strawberry Fields, PennyLane, and plugin versions.
- Record the simulator backend and all backend options.
- Choose the number of modes, gate parameters, cutoff, shots, and random seed before collecting comparison data.
- Keep the circuit description in source control so a later run uses the same preparation, operations, and measurements.
Run the first circuit natively in Strawberry Fields
1. Create a two-mode program
The following minimal sketch prepares one photon in mode 0, applies a beamsplitter, measures both modes, and runs 1,000 samples on a Fock simulator with a cutoff of 5. The operation names and engine API should be checked against your installed Strawberry Fields version.
import strawberryfields as sf
from strawberryfields import ops
prog = sf.Program(2)
with prog.context as q:
ops.Fock(1) | q[0]
ops.BSgate(theta=0.4, phi=0.0) | (q[0], q[1])
ops.MeasureFock() | (q[0], q[1])
eng = sf.Engine("fock", backend_options={"cutoff_dim": 5})
result = eng.run(prog, shots=1000)
print(result.samples)2. Understand each line
sf.Program(2)allocates two optical modes.ops.Fock(1) | q[0]prepares one photon in the first mode. Mode 1 remains in vacuum, giving |1,0⟩.ops.BSgate(theta=0.4, phi=0.0)mixes the two modes. The chosen parameters determine the probability of finding the photon in either output.ops.MeasureFock()requests photon-number-resolving measurements on both modes.cutoff_dim=5retains basis levels from |0⟩ through |4⟩ in each mode for the numerical simulation.shots=1000requests 1,000 repeated measurement outcomes, returned throughresult.samples.
3. Inspect the output
Each sample is an occupation-number tuple for the two modes. In this one-photon experiment, the dominant outcomes should correspond to one photon in one output mode or the other. The exact frequencies depend on the beamsplitter parameters and simulator behavior. Do not treat a single 1,000-shot histogram as an exact probability distribution; it is an estimate with sampling uncertainty.
For a boson-sampling-style workflow, the same result object exposes measured Fock states through its samples attribute after execution. That raw occupation data is the appropriate starting point for histogram, collision, or distributional analyses.
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Run the same idea through PennyLane
The PennyLane-Strawberry Fields plugin exposes separate devices for distinct simulation models. A Fock device can be initialized with a wire count and an explicit cutoff:
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dev = qml.device("strawberryfields.fock", wires=2, cutoff_dim=10)A PennyLane QNode can then prepare a Fock state, apply a beamsplitter, and return an expectation value or probability. The precise operation and measurement syntax should match the PennyLane and plugin versions in your environment. The key point is that cutoff_dim belongs to the simulator representation, not to a physical detector specification.
The Fock simulator represents each mode in the truncated basis |0⟩, |1⟩, …, |D−1⟩. With cutoff_dim=10, the largest represented occupation per mode is 9. Increasing the cutoff adds basis states; it does not change the optical circuit itself.
Analytic and finite-shot QNodes
Use an analytic execution when you want expectation values or probabilities without sampling noise. Use a positive shot count when you want to model repeated measurement and estimate the same quantities from finite data. Keep these modes separate in your records: a disagreement between an analytic value and a sampled estimate is not automatically a simulator error.
The PennyLane Fock device does not support qml.state, qml.sample, or qml.density_matrix. Use supported expectation or probability measurements for that device, or execute a native Strawberry Fields program with Fock measurements when you need raw occupation samples.
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Choose cutoff_dim by convergence, not guesswork
The cutoff is a numerical approximation. It limits the occupation basis retained for every mode, so a value that is adequate for one circuit may be inadequate for another. Higher cutoffs generally improve accuracy, but memory and runtime grow rapidly. PennyLane documents at least an MD state-scaling relationship for M modes and cutoff D; this is a scaling statement, not a measured benchmark for your computer.
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A practical convergence procedure
- Choose the observable or probability that answers your experiment’s question. Examples include an output probability, an expectation value, or a distribution statistic.
- Run the identical circuit at several cutoffs, such as 4, 6, 8, and 10 when those values fit your available memory. Keep modes, gates, measurement, shots, and software fixed.
- Compare adjacent cutoffs using a tolerance selected before looking at the results. For a scalar observable, this can be an absolute difference; for a distribution, use a stated distance or per-outcome tolerance.
- Increase the cutoff until the result changes less than that tolerance for the quantity you intend to report.
- Repeat the check for any additional observable or parameter region used in the study. Convergence of one value does not prove convergence of every output probability.
- Record the chosen cutoff and the convergence table alongside the final result.
If raising the cutoff changes the result substantially, report the calculation as unconverged rather than presenting the lowest-cost run as definitive. If the run exhausts memory, reduce the mode count for a diagnostic test, simplify the circuit, or move to a more suitable backend; do not silently lower the cutoff and retain the same accuracy claim.
Benchmark a photonic circuit fairly
1. Freeze the circuit definition
Write down the number of modes, initial states, every gate and parameter, measurement type, backend, cutoff, shot count, and random seed where the API supports one. A benchmark is not reproducible if a hidden default changes between runs.
2. Separate truncation error from sampling error
First perform an analytic run, using shots=None where supported, to expose the simulator’s value without shot noise. Then perform finite-shot runs with a fixed shot budget. Compare cutoff values in both modes when practical. This lets you distinguish a cutoff change from ordinary variation caused by drawing a finite number of samples.
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For each sampled observable, report the number of shots, estimated mean, standard deviation or confidence interval, and number of independent repetitions. Keep the shot budget identical when comparing two parameter settings. If you use a histogram, include the number of samples behind it and explain whether uncertainty bars are binomial, bootstrap, or another method.
4. Measure computational resources
Record wall-clock time, peak memory when available, mode count, cutoff, shot count, backend, and machine details. A runtime measured on one laptop is not directly comparable with a runtime measured on another computer or cloud worker unless the hardware and software context are reported. State whether compilation, device construction, cache warm-up, and data transfer are included.
5. Use a reference result
Validate the circuit against an analytic result, a larger-cutoff calculation, or a separately validated classical calculation. State which reference you used and what quantity was compared. A reference is a check on correctness, not proof that a hardware device will behave identically.
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6. Keep a benchmark record
| Field | What to record |
|---|---|
| Circuit | Modes, initial states, gate sequence, parameters, and measurement |
| Software | Python, Strawberry Fields, PennyLane, plugin, operating system |
| Backend | Fock, Gaussian, GBS, remote simulator, or hardware service |
| Numerics | Cutoff dimension and any precision or caching options |
| Sampling | Analytic or shot-based execution, shots per run, repetitions, seed |
| Resources | Machine, wall-clock time, peak memory, and whether setup time is included |
| Validation | Reference method, tolerance, and observed difference |
Build a boson-sampling benchmark
Linear-optical boson sampling
For a linear-optical boson-sampling experiment, decompose the interferometer into two-mode beamsplitters and single-mode phase shifters, prepare the chosen input Fock state, and append Fock measurements. Execute enough shots to estimate the output occupation distribution. The measured Fock states are available as samples after execution in native Strawberry Fields workflows.
Benchmark this task with output statistics appropriate to a distribution, not only with a single expectation value. Fix the interferometer, input occupation, cutoff, and shot budget while comparing implementations. If the circuit can produce occupations near the cutoff, include a convergence study; otherwise, a low cutoff can suppress valid output events.
Gaussian boson sampling
Use the GBS device for Gaussian boson sampling rather than treating GBS as an ordinary qubit or non-Gaussian Fock benchmark. The device supports graph embedding, Fock probabilities, cutoff_dim, shot-based execution, and optional caching of generated samples for repeated evaluations.
Document whether samples were generated once and reused through caching or regenerated for each evaluation. Caching can improve repeated evaluations, but it changes the relationship between runtime and random sampling, so cached and uncached measurements should not be compared without disclosure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Move from simulation to hardware
Make the local simulation reproducible before submitting the program remotely. A local run gives you a reference for circuit construction, cutoff behavior, and expected output format. A remote execution is useful when loss, detector statistics, queueing, cloud access, or other hardware effects are part of the research question.
The PennyLane plugin documents remote backends, and Strawberry Fields documents submitting programs for photonic-hardware execution. Availability, pricing, queue times, and geographic access can change, so verify those conditions at the time of use and record the backend name and service date in your results.
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- Save the exact program submitted, including mode count and gate parameters.
- Record the remote backend, account or project context when relevant, submission date, and execution status.
- Report whether the result came from hardware, a remote simulator, or a local simulator.
- Keep shot count and post-processing identical when comparing local and remote output.
- Expect hardware data to include effects absent from an ideal Fock simulation, such as loss and detector statistics; explain which effects your comparison includes or omits.
Troubleshoot common first-run failures
The circuit fails at state preparation
Check that you selected a Fock-capable backend. A Gaussian backend is intended for Gaussian operations and cannot stand in for a circuit whose essential input is a non-Gaussian Fock state.
The API rejects a measurement
Check device-specific measurement support. On the PennyLane Fock device, raw state, sample, and density-matrix measurement calls are unsupported. Return a supported expectation or probability, or use native Strawberry Fields with MeasureFock when you need occupation samples.
Increasing the cutoff changes the answer
Your previous cutoff was not converged for that observable or parameter setting. Continue the cutoff sweep, state the tolerance, and report the first value that satisfies it. Do not interpret the difference as a physical effect until truncation has been controlled.
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Finite shots and random-number generation can produce different estimates. Fix a seed where the API permits it, increase repetitions, and report uncertainty. If analytic runs differ, inspect hidden device state, compilation, caching, and version differences before blaming sampling.
The simulation runs out of memory
State-space cost rises sharply with both mode count and cutoff. Confirm that the requested cutoff is necessary, test a smaller diagnostic circuit, and measure resource use before scaling up. A successful low-cutoff run is not evidence that a larger circuit has converged.
A compact comparison checklist
- Backend type: Fock, Gaussian, GBS, remote simulator, or hardware.
- Representation: mode count and cutoff dimension.
- Execution: analytic or shot-based, with shots and repetitions.
- Measurements: expectations, probabilities, or raw Fock samples.
- Reproducibility: software versions, seed, circuit text, and cache policy.
- Resources: runtime, memory, machine, and setup-time policy.
- Validation: convergence tolerance and independent reference.
- Provenance: local simulation, remote simulator, or hardware, with backend and date.
For most beginners, the native two-mode Fock example is the shortest path to a visible result. The PennyLane Fock device is the better fit when the circuit will become part of a differentiable or hybrid workflow. Boson-sampling and GBS should be treated as separate benchmark families because their natural outputs are sampled occupation distributions, not merely one scalar expectation.
What a publishable first benchmark should show
A credible report includes the circuit definition, software and backend versions, cutoff-convergence evidence, analytic and sampled results when available, a fixed shot budget, uncertainty estimates, resource measurements, and a stated reference. It also identifies unsupported measurement APIs and distinguishes local ideal simulation from remote or hardware execution.
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That discipline turns a short demonstration into a useful benchmark: readers can reproduce the circuit, see whether the numerical representation is converged, determine how much variation comes from finite shots, and understand which conclusions apply only to the chosen simulator or service.
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