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qrc

eigenkit.qml.qrc

Quantum reservoir computing (QRC) estimators, compatible with scikit-learn.

A reservoir is a circuit with fixed random weights, which are never trained. These estimators sample the weights once (with the seed random_state), run the circuit over the input sequences with reservoir.run_reservoir(), extract classical features, and train only a linear readout. This split into a fixed reservoir and a linear readout is the standard form of QRC. So these estimators belong with the scikit-learn estimators (next to QSVC and QSVR), and not in the PyTorch package.

They are thin wrappers around the functions of the reservoir package. For full control (custom processing of the features, custom readouts), call run_reservoir and the feature extractors directly.

Data shapes: X has shape (n_sequences, n_timesteps, n_features), and y has shape (n_sequences, n_timesteps), with one target per timestep (sequence to sequence). washout drops the first timesteps from the fit of the readout, so that the reservoir can forget its initial state. Predictions are still made for every timestep.

QuantumReservoirClassifier

Bases: _ReservoirMixin, BaseQuantumClassifier

Quantum reservoir classifier: a fixed random reservoir and a logistic readout.

It uses the same reservoir as QuantumReservoirRegressor. The readout is a scikit-learn LogisticRegression on the features of each timestep. y holds integer or label targets, of shape (n_sequences, n_timesteps).

Parameters:

Name	Type	Description	Default
blocks	list	The architecture of the reservoir circuit, a list of blocks that is assembled once. Its n_inputs must match the policy: F for the policies that carry the state, and window * F for state_policy="stateless", where F is the number of features per timestep.	required
state_policy	str	"carry" (the unitary of the circuit carries the state from one step to the next), "stateless" (each step starts from |0…0⟩ and encodes a sliding window of inputs), or "leaky" (the carried state is mixed with a reset state and renormalised).	'carry'
window	int	The length of the sliding window, for state_policy="stateless".	1
leak	float	The mixing coefficient alpha, for state_policy="leaky".	1.0
features	FeaturesSpec	The features read from the reservoir. It is "z" (the value of ⟨Z⟩ on each qubit, the default), "probabilities", "amplitudes", a Pauli or Hamiltonian string (for example "0.5 Z0 X1 - 0.3 Y0"), or a list of such strings, to read out any observables.	'z'
C	float	The inverse regularisation strength of the logistic readout, passed to scikit-learn. Smaller values regularise more. This is the only argument that the regressor does not have: there, the readout is a ridge regression controlled by ridge.	1.0
washout	int	The number of timesteps dropped from the fit of the readout. They are not dropped from the predictions.	0
weight_scale	float	The standard deviation of the sampled reservoir weights.	1.0
provider	ProviderSpec	The backend, which must give access to the statevector. Default: "numpy".	'numpy'
random_state	int | None	The seed of the fixed reservoir weights.	42

__sklearn_is_fitted__()

Return True when the readout has been fitted.

fit(X, y)

Fit the estimator on X and y.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required
y	ndarray	One target for each timestep, of shape (n_sequences, n_timesteps).	required

Returns:

Type	Description
'QuantumReservoirClassifier'	This estimator, so that calls can be chained.

predict(X)

Predict a label for every timestep of every sequence in X.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required

Returns:

Type	Description
ndarray	The predicted labels, of shape (n_sequences, n_timesteps).

predict_proba(X)

Predict the class probabilities for every timestep of every sequence in X.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required

Returns:

Type	Description
ndarray	The probabilities, of shape (n_sequences, n_timesteps, n_classes), with the last axis in the order of classes_.

score(X, y)

Score the estimator on X against y.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required
y	ndarray	One target for each timestep, of shape (n_sequences, n_timesteps).	required

Returns:

Type	Description
float	The accuracy of the predictions, over every timestep of every sequence.

QuantumReservoirRegressor

Bases: _ReservoirMixin, BaseQuantumRegressor

Quantum reservoir regressor: a fixed random reservoir and a ridge readout.

Parameters:

Name	Type	Description	Default
blocks	list	The architecture of the reservoir circuit, a list of blocks that is assembled once. Its n_inputs must match the policy: F for the policies that carry the state, and window * F for state_policy="stateless", where F is the number of features per timestep.	required
state_policy	str	"carry" (the unitary of the circuit carries the state from one step to the next), "stateless" (each step starts from |0…0⟩ and encodes a sliding window of inputs), or "leaky" (the carried state is mixed with a reset state and renormalised).	'carry'
window	int	The length of the sliding window, for state_policy="stateless".	1
leak	float	The mixing coefficient alpha, for state_policy="leaky".	1.0
features	FeaturesSpec	The features read from the reservoir. It is "z" (the value of ⟨Z⟩ on each qubit, the default), "probabilities", "amplitudes", a Pauli or Hamiltonian string (for example "0.5 Z0 X1 - 0.3 Y0"), or a list of such strings, to read out any observables.	'z'
ridge	float	The strength of the L2 penalty of the linear readout, which is solved in closed form.	1e-06
washout	int	The number of timesteps dropped from the fit of the readout. They are not dropped from the predictions.	0
weight_scale	float	The standard deviation of the sampled reservoir weights.	1.0
provider	ProviderSpec	The backend, which must give access to the statevector. Default: "numpy".	'numpy'
random_state	int | None	The seed of the fixed reservoir weights.	42

__sklearn_is_fitted__()

Return True when the readout has been fitted.

fit(X, y)

Fit the estimator on X and y.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required
y	ndarray	One target for each timestep, of shape (n_sequences, n_timesteps), or (n_sequences, n_timesteps, n_outputs) for several outputs.	required

Returns:

Type	Description
'QuantumReservoirRegressor'	This estimator, so that calls can be chained.

predict(X)

Predict a value for every timestep of every sequence in X.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required

Returns:

Type	Description
ndarray	The predictions, of shape (n_sequences, n_timesteps), or (n_sequences, n_timesteps, n_outputs) when the targets had several outputs.

score(X, y)

Score the estimator on X against y.

Parameters:

Name	Type	Description	Default
X	ndarray	The sequences, of shape (n_sequences, n_timesteps, n_features). A 2-D array is rejected.	required
y	ndarray	One target for each timestep, of shape (n_sequences, n_timesteps).	required

Returns:

Type	Description
float	The coefficient of determination R² of the predictions, over every timestep of every sequence.