## `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). | | `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. | #### `__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). | | `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. | #### `__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. |