Quantum models

QuantumModel(circuit, observable=None, backend=BackendName.PYQTORCH, diff_mode=DiffMode.AD, measurement=None, noise=None, mitigation=None, configuration=None)

Bases: Module

The central class of qadence that executes QuantumCircuits and make them differentiable.

This class should be used as base class for any new quantum model supported in the qadence framework for information on the implementation of custom models see here.

Initialize a generic QuantumModel instance.

PARAMETER	DESCRIPTION
circuit	The circuit that is executed. TYPE: QuantumCircuit
observable	Optional observable(s) that are used only in the expectation method. You can also provide observables on the fly to the expectation call directly. TYPE: list[AbstractBlock] | AbstractBlock | None DEFAULT: None
backend	A backend for circuit execution. TYPE: BackendName | str DEFAULT: PYQTORCH
diff_mode	A differentiability mode. Parameter shift based modes work on all backends. AD based modes only on PyTorch based backends. TYPE: DiffMode DEFAULT: AD
measurement	Optional measurement protocol. If None, use exact expectation value with a statevector simulator. TYPE: Measurements | None DEFAULT: None
configuration	Configuration for the backend. TYPE: BackendConfiguration | dict | None DEFAULT: None
noise	A noise model to use. TYPE: Noise | None DEFAULT: None

RAISES	DESCRIPTION
ValueError	if the diff_mode argument is set to None

Source code in qadence/model.py

def __init__(
    self,
    circuit: QuantumCircuit,
    observable: list[AbstractBlock] | AbstractBlock | None = None,
    backend: BackendName | str = BackendName.PYQTORCH,
    diff_mode: DiffMode = DiffMode.AD,
    measurement: Measurements | None = None,
    noise: Noise | None = None,
    mitigation: Mitigations | None = None,
    configuration: BackendConfiguration | dict | None = None,
):
    """Initialize a generic QuantumModel instance.

    Arguments:
        circuit: The circuit that is executed.
        observable: Optional observable(s) that are used only in the `expectation` method. You
            can also provide observables on the fly to the expectation call directly.
        backend: A backend for circuit execution.
        diff_mode: A differentiability mode. Parameter shift based modes work on all backends.
            AD based modes only on PyTorch based backends.
        measurement: Optional measurement protocol. If None, use
            exact expectation value with a statevector simulator.
        configuration: Configuration for the backend.
        noise: A noise model to use.

    Raises:
        ValueError: if the `diff_mode` argument is set to None
    """
    super().__init__()

    if not isinstance(circuit, QuantumCircuit):
        TypeError(
            f"The circuit should be of type '<class QuantumCircuit>'. Got {type(circuit)}."
        )

    if diff_mode is None:
        raise ValueError("`diff_mode` cannot be `None` in a `QuantumModel`.")

    self.backend = backend_factory(
        backend=backend, diff_mode=diff_mode, configuration=configuration
    )

    if isinstance(observable, list) or observable is None:
        observable = observable
    else:
        observable = [observable]

    def _is_feature_param(p: Parameter) -> bool:
        return not p.trainable and not p.is_number

    if observable is None:
        self.inputs = list(filter(_is_feature_param, circuit.unique_parameters))
    else:
        uparams = unique_parameters(chain(circuit.block, *observable))
        self.inputs = list(filter(_is_feature_param, uparams))

    conv = self.backend.convert(circuit, observable)
    self.embedding_fn = conv.embedding_fn
    self._circuit = conv.circuit
    self._observable = conv.observable
    self._backend_name = backend
    self._diff_mode = diff_mode
    self._measurement = measurement
    self._noise = noise
    self._mitigation = mitigation
    self._params = nn.ParameterDict(
        {
            str(key): nn.Parameter(val, requires_grad=val.requires_grad)
            for key, val in conv.params.items()
        }
    )

in_features: int property

Number of inputs.

num_vparams: int property

The number of variational parameters.

out_features: int | None property

Number of outputs.

vals_vparams: Tensor property

Dictionary with parameters which are actually updated during optimization.

assign_parameters(values)

Return the final, assigned circuit that is used in e.g. backend.run.

Source code in qadence/model.py

def assign_parameters(self, values: dict[str, Tensor]) -> Any:
    """Return the final, assigned circuit that is used in e.g. `backend.run`."""
    params = self.embedding_fn(self._params, values)
    return self.backend.assign_parameters(self._circuit, params)

expectation(values={}, observable=None, state=None, measurement=None, noise=None, mitigation=None, endianness=Endianness.BIG)

Compute expectation using the given backend.

RETURNS	DESCRIPTION
Tensor	A torch.Tensor of shape n_batches x n_obs

Source code in qadence/model.py

def expectation(
    self,
    values: dict[str, Tensor] = {},
    observable: list[ConvertedObservable] | ConvertedObservable | None = None,
    state: Optional[Tensor] = None,
    measurement: Measurements | None = None,
    noise: Noise | None = None,
    mitigation: Mitigations | None = None,
    endianness: Endianness = Endianness.BIG,
) -> Tensor:
    """Compute expectation using the given backend.

    Returns:
        A torch.Tensor of shape n_batches x n_obs
    """
    if observable is None:
        if self._observable is None:
            raise ValueError(
                "Provide an AbstractBlock as the observable to compute expectation."
                "Either pass a 'native_observable' directly to 'QuantumModel.expectation'"
                "or pass a (non-native) '<class AbstractBlock>' to the 'QuantumModel.__init__'."
            )
        observable = self._observable

    params = self.embedding_fn(self._params, values)
    if measurement is None:
        measurement = self._measurement
    if noise is None:
        noise = self._noise
    else:
        self._noise = noise
    if mitigation is None:
        mitigation = self._mitigation
    return self.backend.expectation(
        circuit=self._circuit,
        observable=observable,
        param_values=params,
        state=state,
        measurement=measurement,
        noise=noise,
        mitigation=mitigation,
        endianness=endianness,
    )

reset_vparams(values)

Reset all the variational parameters with a given list of values.

Source code in qadence/model.py

def reset_vparams(self, values: Sequence) -> None:
    """Reset all the variational parameters with a given list of values."""
    current_vparams = OrderedDict({k: v for k, v in self._params.items() if v.requires_grad})

    assert (
        len(values) == self.num_vparams
    ), "Pass an iterable with the values of all variational parameters"
    for i, k in enumerate(current_vparams.keys()):
        current_vparams[k].data = torch.tensor([values[i]])

Bases: QuantumModel

Quantum neural network model for n-dimensional inputs.

Examples:

import torch
from qadence import QuantumCircuit, QNN, Z
from qadence import hea, feature_map, hamiltonian_factory, kron

# create the circuit
n_qubits, depth = 2, 4
fm = kron(
    feature_map(1, support=(0,), param="x"),
    feature_map(1, support=(1,), param="y")
)
ansatz = hea(n_qubits=n_qubits, depth=depth)
circuit = QuantumCircuit(n_qubits, fm, ansatz)
obs_base = hamiltonian_factory(n_qubits, detuning=Z)

# the QNN will yield two outputs
obs = [2.0 * obs_base, 4.0 * obs_base]

# initialize and use the model
qnn = QNN(circuit, obs, inputs=["x", "y"])
y = qnn(torch.rand(3, 2))

tensor([[-0.2023, -0.4046],
        [-1.0286, -2.0572],
        [-0.3534, -0.7069]], grad_fn=<CatBackward0>)

Initialize the QNN.

The number of inputs is determined by the feature parameters in the input quantum circuit while the number of outputs is determined by how many observables are provided as input

PARAMETER	DESCRIPTION
circuit	The quantum circuit to use for the QNN. TYPE: QuantumCircuit
observable	The observable. TYPE: list[AbstractBlock] | AbstractBlock
backend	The chosen quantum backend. TYPE: BackendName DEFAULT: PYQTORCH
diff_mode	The differentiation engine to use. Choices 'gpsr' or 'ad'. TYPE: DiffMode DEFAULT: AD
measurement	optional measurement protocol. If None, use exact expectation value with a statevector simulator TYPE: Measurements | None DEFAULT: None
noise	A noise model to use. TYPE: Noise | None DEFAULT: None
configuration	optional configuration for the backend TYPE: BackendConfiguration | dict | None DEFAULT: None
inputs	List that indicates the order of variables of the tensors that are passed to the model. Given input tensors xs = torch.rand(batch_size, input_size:=2) a QNN with inputs=["t", "x"] will assign t, x = xs[:,0], xs[:,1]. TYPE: list[Basic | str] | None DEFAULT: None
input_diff_mode	The differentiation mode for the input tensor. TYPE: InputDiffMode | str DEFAULT: AD

Source code in qadence/ml_tools/models.py

def __init__(
    self,
    circuit: QuantumCircuit,
    observable: list[AbstractBlock] | AbstractBlock,
    backend: BackendName = BackendName.PYQTORCH,
    diff_mode: DiffMode = DiffMode.AD,
    measurement: Measurements | None = None,
    noise: Noise | None = None,
    configuration: BackendConfiguration | dict | None = None,
    inputs: list[sympy.Basic | str] | None = None,
    input_diff_mode: InputDiffMode | str = InputDiffMode.AD,
):
    """Initialize the QNN.

    The number of inputs is determined by the feature parameters in the input
    quantum circuit while the number of outputs is determined by how many
    observables are provided as input

    Args:
        circuit: The quantum circuit to use for the QNN.
        observable: The observable.
        backend: The chosen quantum backend.
        diff_mode: The differentiation engine to use. Choices 'gpsr' or 'ad'.
        measurement: optional measurement protocol. If None,
            use exact expectation value with a statevector simulator
        noise: A noise model to use.
        configuration: optional configuration for the backend
        inputs: List that indicates the order of variables of the tensors that are passed
            to the model. Given input tensors `xs = torch.rand(batch_size, input_size:=2)` a QNN
            with `inputs=["t", "x"]` will assign `t, x = xs[:,0], xs[:,1]`.
        input_diff_mode: The differentiation mode for the input tensor.
    """
    super().__init__(
        circuit,
        observable=observable,
        backend=backend,
        diff_mode=diff_mode,
        measurement=measurement,
        configuration=configuration,
        noise=noise,
    )
    if self._observable is None:
        raise ValueError("You need to provide at least one observable in the QNN constructor")
    if (inputs is not None) and (len(self.inputs) == len(inputs)):
        self.inputs = [sympy.symbols(x) if isinstance(x, str) else x for x in inputs]  # type: ignore[union-attr]
    elif (inputs is None) and len(self.inputs) <= 1:
        self.inputs = [sympy.symbols(x) if isinstance(x, str) else x for x in self.inputs]  # type: ignore[union-attr]
    else:
        raise ValueError(
            """
            Your QNN has more than one input. Please provide a list of inputs in the order of
            your tensor domain. For example, if you want to pass
            `xs = torch.rand(batch_size, input_size:=3)` to you QNN, where
            ```
            t = x[:,0]
            x = x[:,1]
            y = x[:,2]
            ```
            you have to specify
            ```
            QNN(circuit, observable, inputs=["t", "x", "y"])
            ```
            You can also pass a list of sympy symbols.
        """
        )
    self.format_to_dict = format_to_dict_fn(self.inputs)  # type: ignore[arg-type]
    self.input_diff_mode = InputDiffMode(input_diff_mode)
    if self.input_diff_mode == InputDiffMode.FD:
        from qadence.backends.utils import finitediff

        self.__derivative = finitediff
    elif self.input_diff_mode == InputDiffMode.AD:
        self.__derivative = _torch_derivative  # type: ignore[assignment]
    else:
        raise ValueError(f"Unkown forward diff mode: {self.input_diff_mode}")

forward(values=None, state=None, measurement=None, noise=None, endianness=Endianness.BIG)

Forward pass of the model.

This returns the (differentiable) expectation value of the given observable operator defined in the constructor. Differently from the base QuantumModel class, the QNN accepts also a tensor as input for the forward pass. The tensor is expected to have shape: n_batches x in_features where n_batches is the number of data points and in_features is the dimensionality of the problem

The output of the forward pass is the expectation value of the input observable(s). If a single observable is given, the output shape is n_batches while if multiple observables are given the output shape is instead n_batches x n_observables

PARAMETER	DESCRIPTION
values	the values of the feature parameters TYPE: dict[str, Tensor] | Tensor DEFAULT: None
state	Initial state. TYPE: Tensor | None DEFAULT: None
measurement	optional measurement protocol. If None, use exact expectation value with a statevector simulator TYPE: Measurements | None DEFAULT: None
noise	A noise model to use. TYPE: Noise | None DEFAULT: None
endianness	Endianness of the resulting bit strings. TYPE: Endianness DEFAULT: BIG

RETURNS	DESCRIPTION
Tensor	a tensor with the expectation value of the observables passed in the constructor of the model TYPE: Tensor

Source code in qadence/ml_tools/models.py

def forward(
    self,
    values: dict[str, Tensor] | Tensor = None,
    state: Tensor | None = None,
    measurement: Measurements | None = None,
    noise: Noise | None = None,
    endianness: Endianness = Endianness.BIG,
) -> Tensor:
    """Forward pass of the model.

    This returns the (differentiable) expectation value of the given observable
    operator defined in the constructor. Differently from the base QuantumModel
    class, the QNN accepts also a tensor as input for the forward pass. The
    tensor is expected to have shape: `n_batches x in_features` where `n_batches`
    is the number of data points and `in_features` is the dimensionality of the problem

    The output of the forward pass is the expectation value of the input
    observable(s). If a single observable is given, the output shape is
    `n_batches` while if multiple observables are given the output shape
    is instead `n_batches x n_observables`

    Args:
        values: the values of the feature parameters
        state: Initial state.
        measurement: optional measurement protocol. If None,
            use exact expectation value with a statevector simulator
        noise: A noise model to use.
        endianness: Endianness of the resulting bit strings.

    Returns:
        Tensor: a tensor with the expectation value of the observables passed
            in the constructor of the model
    """
    return self.expectation(
        values, state=state, measurement=measurement, noise=noise, endianness=endianness
    )

from_configs(register, fm_config, ansatz_config, obs_config) classmethod

Create a QNN from a set of configurations.

PARAMETER	DESCRIPTION
register	The number of qubits or a register object. TYPE: int | Register
fm_config	The configuration for the feature map. TYPE: Any
ansatz_config	The configuration for the ansatz. TYPE: Any
obs_config	The configuration for the observable. TYPE: Any

RETURNS	DESCRIPTION
QNN	A QNN object.

Example:

import torch
from qadence.ml_tools.config import AnsatzConfig, FeatureMapConfig
from qadence.ml_tools import QNN
from qadence.constructors import ObservableConfig
from qadence.operations import Z
from qadence.types import (
    AnsatzType, BasisSet, ReuploadScaling, ObservableTransform, Strategy
)

register = 4
fm_config = FeatureMapConfig(
    num_features=2,
    inputs=["x", "y"],
    basis_set=BasisSet.FOURIER,
    reupload_scaling=ReuploadScaling.CONSTANT,
    feature_range={
        "x": (-1.0, 1.0),
        "y": (0.0, 1.0),
    },
)
ansatz_config = AnsatzConfig(
    depth=2,
    ansatz_type=AnsatzType.HEA,
    ansatz_strategy=Strategy.DIGITAL,
)
obs_config = ObservableConfig(
    detuning=Z,
    scale=5.0,
    shift=0.0,
    transformation_type=ObservableTransform.SCALE,
    trainable_transform=None,
)

qnn = QNN.from_configs(register, fm_config, ansatz_config, obs_config)

x = torch.rand(2, 2)
y = qnn(x)

tensor([[2.7665],
        [0.2073]], grad_fn=<CatBackward0>)

Source code in qadence/ml_tools/models.py

@classmethod
def from_configs(
    cls,
    register: int | Register,
    fm_config: Any,
    ansatz_config: Any,
    obs_config: Any,
) -> QNN:
    """Create a QNN from a set of configurations.

    Args:
        register: The number of qubits or a register object.
        fm_config: The configuration for the feature map.
        ansatz_config: The configuration for the ansatz.
        obs_config: The configuration for the observable.

    Returns:
        A QNN object.

    Example:
    ```python exec="on" source="material-block" result="json"
    import torch
    from qadence.ml_tools.config import AnsatzConfig, FeatureMapConfig
    from qadence.ml_tools import QNN
    from qadence.constructors import ObservableConfig
    from qadence.operations import Z
    from qadence.types import (
        AnsatzType, BasisSet, ReuploadScaling, ObservableTransform, Strategy
    )

    register = 4
    fm_config = FeatureMapConfig(
        num_features=2,
        inputs=["x", "y"],
        basis_set=BasisSet.FOURIER,
        reupload_scaling=ReuploadScaling.CONSTANT,
        feature_range={
            "x": (-1.0, 1.0),
            "y": (0.0, 1.0),
        },
    )
    ansatz_config = AnsatzConfig(
        depth=2,
        ansatz_type=AnsatzType.HEA,
        ansatz_strategy=Strategy.DIGITAL,
    )
    obs_config = ObservableConfig(
        detuning=Z,
        scale=5.0,
        shift=0.0,
        transformation_type=ObservableTransform.SCALE,
        trainable_transform=None,
    )

    qnn = QNN.from_configs(register, fm_config, ansatz_config, obs_config)

    x = torch.rand(2, 2)
    y = qnn(x)
    print(str(y)) # markdown-exec: hide
    ```
    """
    from .constructors import build_qnn_from_configs

    return build_qnn_from_configs(register, fm_config, ansatz_config, obs_config)