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What a photonic quantum circuit is
A photonic quantum circuit processes information in optical modes rather than qubits. A mode can be a spatial path, time bin, frequency channel, or another distinguishable optical degree of freedom. The circuit applies continuous-variable operations such as state preparation, phase shifts, squeezing, and two-mode interferometer elements, followed by a measurement.
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Strawberry Fields represents a circuit as a Program. The program declares the number of modes, operations are applied inside its context, and an engine executes it on a simulator or a remote backend. The Strawberry Fields documentation summarizes the model as: To construct a photonic quantum circuit in Strawberry Fields, a
Program object must be created, and operations applied.
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Why begin with a two-mode non-Gaussian circuit
The state |1,0⟩ contains one photon in the first mode and vacuum in the second. A beamsplitter creates a controlled superposition of where that photon can be detected. The experiment is small enough to understand and inspect, but it is non-Gaussian, so it tests the Fock-state simulation path rather than only Gaussian phase-space calculations.
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PennyLane’s official hybrid-computation example uses this pattern and notes that the Strawberry Fields Fock backend is required for the non-Gaussian state. A Gaussian backend is appropriate for circuits made entirely from Gaussian operations and compatible measurements, but it is not the right first choice for a Fock-state input such as |1,0⟩.
Choose the execution route
Use a native Strawberry Fields program when you want direct access to Fock samples and the clearest correspondence between the circuit and its measured occupation numbers. Use the PennyLane plugin when the photonic circuit will become part of a differentiable or hybrid quantum-classical workflow.
| Route | Backend | Best use | Important controls or limits |
|---|---|---|---|
| Native Strawberry Fields | Fock simulator | Non-Gaussian states, occupation samples, direct photonic programs | Set the engine cutoff; inspect the result object for samples |
| PennyLane plugin | strawberryfields.fock |
Full continuous-variable simulation in PennyLane | Declare wires and cutoff_dim; supported measurements differ from native execution |
| PennyLane plugin | strawberryfields.gaussian |
Gaussian operations and photon-number-resolving measurements | Not a substitute for Fock simulation of non-Gaussian state preparation |
| PennyLane plugin | strawberryfields.gbs |
Gaussian boson sampling and graph-embedding experiments | Use sampling and probability statistics suited to GBS |
| PennyLane plugin | strawberryfields.remote |
Remote photonic backends | Availability, queueing, pricing, and geography can change |
The PennyLane-Strawberry Fields plugin documentation identifies release 0.29.1 and compatibility with PennyLane versions up to and including 0.29. Those version details are historical documentation facts, not a guarantee that a newly installed environment will expose identical APIs. Pin the versions in your own environment and check the installed documentation before relying on a gate or engine signature.
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Run the first circuit natively in Strawberry Fields
1. Create a two-mode program
The following minimal program prepares |1,0⟩, applies a beamsplitter with angle 0.4 and phase 0.0, measures both modes in the Fock basis, and requests 1,000 samples.
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)The names and call signatures above follow the documented Program/context/measurement pattern, but exact gate and engine APIs should be checked against the Strawberry Fields version installed on your machine. The critical elements are the two-mode Program, Fock preparation, a two-mode beamsplitter, Fock measurement, a Fock engine, and an explicit cutoff.
2. Understand the output
result.samples is a collection of measured occupation-number outcomes. Each shot contains one occupation value per mode. For the one-photon input, outcomes should be concentrated on configurations in which the total detected photon number is consistent with one photon, subject to the simulator model and any operations you add. Do not infer a physical detector limit from the largest number you happen to see; the simulator’s cutoff is a numerical truncation of the state basis.
3. Change the beamsplitter deliberately
The parameter theta controls the mixing strength and phi supplies a phase. Keep the circuit, cutoff, and shot count fixed while changing one parameter. A fair parameter sweep records the measured probability or expectation for every setting and uses the same uncertainty procedure for each point.
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The plugin exposes the Fock simulator through a PennyLane device declaration such as:
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import pennylane as qml
dev = qml.device('strawberryfields.fock', wires=2, cutoff_dim=10)The device stores each mode in the truncated basis |0⟩, |1⟩, …, |D−1⟩, where D is cutoff_dim. A PennyLane QNode can then prepare a Fock state, apply a beamsplitter, and return a supported expectation or probability measurement. Operation names and measurement constructors can vary across PennyLane and plugin releases, so inspect the version-matched API reference rather than copying a constructor blindly.
When PennyLane is the better choice
- Use it when a classical optimizer, automatic differentiation, or another PennyLane device will be part of the same workflow.
- Use the Fock device for non-Gaussian state preparation and full continuous-variable simulation.
- Use the Gaussian device for Gaussian circuits where its specialized representation is sufficient.
- Use the GBS device for graph-embedding operations and Gaussian boson-sampling probabilities or samples.
For raw occupation samples, native Strawberry Fields is often simpler. On the PennyLane Fock device, qml.state, qml.sample, and qml.density_matrix are not supported. Use supported expectation or probability measurements, or execute a native Strawberry Fields program with Fock measurements when the individual occupation records are essential.
Choose cutoff_dim correctly
cutoff_dim is a numerical approximation control. It keeps basis states from |0⟩ through |D−1⟩ in each mode; it is not a claim that a physical detector can only resolve D photon numbers. Increasing D allows higher-occupation components to be represented, but increases memory use and execution time.
The PennyLane Fock documentation describes minimum tracked-state scaling as MD for M modes and cutoff D. This is a scaling relationship, not a hardware benchmark or a promise that every implementation will consume exactly that amount of memory. More modes, stronger squeezing, larger photon-number inputs, and operations that populate high occupations all make a larger cutoff more important.
A practical convergence procedure
- Choose the observable or probability that answers your scientific question before selecting a cutoff.
- Run the unchanged circuit at a small starting cutoff, then repeat at successively larger values such as 5, 8, 10, 12, or another sequence that fits your memory budget.
- Compare the target quantity between adjacent cutoffs. Use an absolute or relative tolerance chosen in advance.
- Stop only when the quantity remains within that tolerance across a further increase. If it does not stabilize, the cutoff is still too small or the calculation is too resource-intensive for the chosen representation.
- Record the cutoff, observable, tolerance, and values in the benchmark report.
Convergence is observable-specific. A low-photon-number probability can appear stable while a tail probability, high-order moment, or gradient is still changing. If your paper or application uses several quantities, test each one that matters.
Symptoms of an insufficient cutoff
- Probabilities or expectations move materially when D increases.
- Results depend on the cutoff even though shots and the random seed are unchanged.
- High-occupation outcomes are artificially clipped at the top of the basis.
- Runtime appears unexpectedly low because the state representation is too aggressively truncated.
Benchmark the circuit fairly
A benchmark is more than a runtime number. It must distinguish numerical truncation, finite-shot noise, and machine-dependent performance. Freeze the experiment first, then vary one factor at a time.
1. Freeze the circuit definition
Record the number of modes, input states, every gate and parameter, measurement type, software versions, backend, cutoff dimension, shot count, and random seed whenever the API supports one. Save the source code and environment information with the result. A benchmark that omits the cutoff or backend cannot be reproduced meaningfully.
2. Establish cutoff convergence
Use the convergence procedure above before comparing implementations or parameter settings. If one result uses D=5 and another uses D=15 without evidence that both are converged, the comparison mixes algorithmic accuracy with performance.
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3. Separate analytic and sampled execution
With shots=None, the PennyLane Fock device returns analytic expectation values. With a positive shot count, it estimates observables from circuit evaluations. Analytic execution removes shot noise but does not remove truncation error. Shot-based execution adds sampling uncertainty on top of whatever approximation the simulator uses. Report both when possible so these two error sources are not conflated.
4. Report statistical uncertainty
For every sampled observable, state the number of shots, the sample mean, a standard deviation or confidence interval, and the number of independent repetitions. Keep the shot budget fixed when comparing two parameter settings or two implementations. If repetitions use independent seeds, say so; if samples are reused through a cache, say that instead.
5. Measure resource cost
Record wall-clock time, peak memory when available, mode count, cutoff, and shots. State the processor, memory, operating system, Python version, simulator version, and whether compilation or warm-up time is included. Do not compare runtimes from different machines as if they were hardware-independent algorithmic results.
6. Use a reference result
Compare each result with an analytic value, a larger-cutoff calculation, or a separately validated classical calculation. Name the reference and explain whether it tests truncation, sampling, or both. A larger cutoff is useful for checking convergence but is not automatically an exact answer.
Analytic values and samples answer different questions
An analytic run asks what the simulator predicts from its truncated state representation without drawing a finite sample set. A shot-based run asks what an experimenter would estimate after a specified number of executions. Neither replaces the other.
| Question | Preferred run | What to report |
|---|---|---|
| Has the Fock representation converged? | Analytic runs at several cutoffs | Cutoff sequence, target quantity, tolerance, and differences |
| How noisy is an estimated expectation? | Fixed-shot repetitions | Shots, mean, spread or interval, and repetitions |
| What occupation patterns are produced? | Native Fock measurements or a supported sampling workflow | Samples, shot count, and any post-processing rule |
| How expensive is execution? | Timed runs after defining warm-up policy | Wall time, peak memory, machine, software, modes, cutoff, and shots |
Do not compare an analytic expectation from one cutoff with a sampled estimate from another and call the difference a physical effect. First align the representation and then quantify sampling uncertainty.
Build a boson-sampling benchmark
For a linear-optical boson-sampling experiment, replace the single beamsplitter with an interferometer. Decompose the interferometer into two-mode beamsplitters and single-mode phase shifters, prepare the chosen input Fock pattern, and append Fock measurements. After execution, the measured occupation-number records are available through the result object’s samples attribute in the native workflow.
What to hold fixed
- Number of modes and input photons.
- Interferometer decomposition and every beamsplitter and phase parameter.
- Fock cutoff and any loss or noise model.
- Number of shots and random-seed policy.
- Post-processing, including whether collisions are retained, discarded, or grouped.
Use output statistics suited to the question: individual sample frequencies, collision rates, marginal occupation probabilities, or a validated distance between distributions. State the statistic before looking at the results, because changing it after seeing the samples can bias the comparison.
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Run a Gaussian boson-sampling variant
The Strawberry Fields GBS device is designed for Gaussian boson sampling. The documented feature set includes graph-embedding operations, Fock probabilities, cutoff_dim, shot-based execution, and optional caching of generated samples for repeated evaluations.
GBS has a different input model from the |1,0⟩ non-Gaussian demonstration. Use the GBS backend when squeezed-state inputs and graph-embedding operations are the object of study, not merely because it also produces photon-number samples.
Benchmarking cached versus uncached evaluations
If you enable sample caching for repeated evaluations, report that choice explicitly. Caching can remove repeated sample generation from later evaluations, so a cached timing measures a different workload from a fresh-sampling timing. Compare like with like: either disable caching for all runs or separate first-evaluation and cached-evaluation costs.
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Know the measurement restrictions
The PennyLane Fock device does not support qml.state, qml.sample, or qml.density_matrix. This matters when a tutorial assumes qubit-style access to a complete state vector or raw samples. Use a supported expectation or probability measurement for a PennyLane-only experiment. If the research question requires raw occupation records, execute a native Strawberry Fields program with MeasureFock and inspect result.samples.
A measurement API restriction is not the same as a physics limitation. It describes what that device interface returns to the caller. Changing to a native program or a different supported backend can change the available output without changing the optical circuit.
Move from simulation to photonic hardware
Make the local simulation reproducible before submitting anything remotely. A remote backend is useful when loss, detector statistics, calibration drift, queueing, or cloud execution is part of the research question. It is not automatically a more accurate version of the local Fock simulator.
- Validate the circuit locally with a pinned software environment and a demonstrated cutoff convergence.
- Define the hardware-facing question: ideal sampling, loss behavior, detector statistics, or end-to-end service performance.
- Submit the same logical program to the documented remote backend, recording backend name, date, region if relevant, queue delay, execution time, shots, and any device settings.
- Repeat enough times to separate run-to-run variation from the effect you are studying.
- Compare hardware output with a clearly identified reference, such as the converged local simulation or a noise-aware model.
Remote availability, pricing, queueing, and geographic access are volatile. Check the current plugin and Strawberry Fields service documentation at the time of execution and preserve the backend metadata with your data.
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The circuit runs out of memory
Reduce the number of modes, lower the cutoff temporarily, simplify the state preparation, or run fewer parameter points. Then perform a convergence study on the reduced problem. Do not silently keep the smaller cutoff in a final benchmark unless the target observable has been shown to be stable.
Results change when only the cutoff changes
Treat this as truncation error, not as a physical effect. Increase the cutoff until the target quantity meets the predefined tolerance, or report that the calculation has not converged.
You cannot obtain raw samples in PennyLane
This is expected for the PennyLane Fock device because the raw sample and state APIs listed above are unsupported. Use supported probabilities or expectations, or switch the sampling portion to a native Strawberry Fields program with Fock measurements.
The code fails after an upgrade
Check the installed Strawberry Fields, PennyLane, and plugin versions, then compare the gate and engine signatures with the documentation for that exact release. Pin a known-compatible environment for a benchmark and include those versions in the report.
Two machines produce different timings
Keep the circuit and numerical settings fixed, but report processor, memory, operating system, Python and library versions, thread settings when relevant, and whether compilation or initialization is included. A timing without this context is not portable.
Remote jobs appear inconsistent
Record backend identity, submission date, queue delay, shot count, device settings, and any service-side calibration information exposed by the API. Separate queue time from execution time and repeat the job before attributing a difference to the circuit.
A reproducible report template
A useful benchmark record can be organized as follows:
- Circuit: modes, input state, operation sequence, parameters, and measurement.
- Software: Python, Strawberry Fields, PennyLane, plugin, and operating-system versions.
- Backend: Fock, Gaussian, GBS, or remote; include the device name and execution date.
- Numerics: cutoff dimension, analytic or shot-based mode, shots, repetitions, seed policy, and caching.
- Accuracy: target observables, convergence tolerance, cutoff sweep, and reference calculation.
- Resources: wall-clock time, peak memory, processor, and mode count.
- Results: means, uncertainty intervals, sample frequencies or probabilities, and any failed or discarded runs with reasons.
This record lets another reader distinguish a change in circuit behavior from a change in truncation, sampling, software, hardware, or service conditions.
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A successful first run is not just a printed array of samples. It demonstrates that the program can be reconstructed, the non-Gaussian state is being simulated by the Fock backend, the chosen observable is stable at the reported cutoff, and the uncertainty from finite shots is quantified separately. Once those conditions are met, the same workflow scales conceptually to larger interferometers, GBS experiments, differentiable PennyLane models, and carefully documented remote executions.
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