backends

`eigenkit.photonics.backends`

The photonic backends, which are executors that implement Executor.

Executor is the protocol defined in photonics.executor.

`PercevalBackend`

The photonic executor that runs on a local Perceval simulator.

compile(spec) returns a PercevalCompiled.

Parameters:

Name	Type	Description	Default
`backend_name`	`str`	A Perceval backend that computes the full output distribution. `"SLOS"` (the default) and `"Naive"` are exact, while `"MPS"` and `"NaiveApprox"` approximate the distribution, and so the gradients too. Backends that only sample, such as `"CliffordClifford2017"`, are rejected, because `run` returns probabilities, not samples.	`'SLOS'`

Raises:

Type	Description
`ValueError`	If `backend_name` is unknown or only samples.

`compile(spec)`

Wrap spec in a reusable handle. Perceval runs at each call of the handle.

Parameters:

Name	Type	Description	Default
`spec`	`PhotonicCircuitSpec`	The circuit to compile.	required

Returns:

Type	Description
`PercevalCompiled`	The `PercevalCompiled` handle for `spec`.

`PercevalCompiled`

A reusable handle to a compiled photonic circuit, cheap to call many times.

It is the linear-optics counterpart of CompiledCircuit. PercevalBackend.compile builds it.

Parameters:

Name	Type	Description	Default
`spec`	`PhotonicCircuitSpec`	The circuit that this handle evaluates.	required
`backend_name`	`str`	The Perceval backend, as for `PercevalBackend`. Default: `"SLOS"`.	`'SLOS'`

Raises:

Type	Description
`ValueError`	If `backend_name` is unknown or only samples.

`expectation(weights, inputs, observable)`

Return the expectation value of a PhotonicObservable, as a single number.

The observable is diagonal in the Fock basis.

Parameters:

Name	Type	Description	Default
`weights`	`ndarray`	The 1-D weight vector.	required
`inputs`	`ndarray`	The 1-D input vector.	required
`observable`	`PhotonicObservable`	A `PhotonNumber` or a `FockProbability`.	required

Raises:

Type	Description
`ValueError`	If `observable` can never match the output of the circuit.

`jacobian(weights, inputs, observable, *, method='parameter_shift', need_weight_grad=True, need_input_grad=True)`

Return (d<O>/d weights, d<O>/d inputs) for an observable.

The observable is diagonal in the Fock basis. Only one sample is accepted (1-D weights and inputs).

Parameters:

Name	Type	Description	Default
`weights`	`ndarray`	The 1-D weight vector.	required
`inputs`	`ndarray`	The 1-D input vector.	required
`observable`	`PhotonicObservable`	A `PhotonNumber` or a `FockProbability`.	required
`method`	`str`	`"parameter_shift"` for the exact photonic parameter-shift rule (in `gradients.py`), or `"finite_difference"` for central finite differences, the reference method. Default: `"parameter_shift"`.	`'parameter_shift'`
`need_weight_grad`	`bool`	If True, compute the gradient with respect to the weights. If False, it is skipped, and `None` is returned in its place.	`True`
`need_input_grad`	`bool`	If True, compute the gradient with respect to the inputs. If False, it is skipped, and `None` is returned in its place.	`True`

Raises:

Type	Description
`ValueError`	If `method` is neither of these two values.

`run(weights, inputs)`

Return the output distribution of photon numbers.

The distribution is a dictionary {photon_counts_tuple: probability}. Fock states with probability zero may be left out, because Perceval's prob_distribution is sparse.

Parameters:

Name	Type	Description	Default
`weights`	`ndarray`	The 1-D weight vector, of length `spec.n_weights`.	required
`inputs`	`ndarray`	The 1-D input vector, of length `spec.n_inputs`.	required

Raises:

Type	Description
`ValueError`	If `weights` or `inputs` has the wrong shape.