qiboml.models#

Submodules#

qiboml.models.encoding#

class qiboml.models.encoding.QuantumEncoding(nqubits: int, qubits: Optional[tuple[int]] = None, _circuit: Circuit = None)[source]#

Bases: ABC

Abstract Encoder class.

Parameters:

nqubits (int) – total number of qubits.
qubits (tuple[int], optional) – set of qubits it acts on, by default range(nqubits).

nqubits: int#

qubits: Optional[tuple[int]] = None#

_circuit: Circuit = None#

abstract __call__(x: ndarray[Any, dtype[_ScalarType_co]]) → Circuit[source]#: Abstract call method.

property circuit: Circuit#: Internal initialized circuit.

property differentiable: bool#: Whether the encoder is differentiable. If True the gradient w.r.t. the inputs is calculated, otherwise it is automatically set to zero.

_abc_impl = <_abc._abc_data object>#

class qiboml.models.encoding.PhaseEncoding(nqubits: int, qubits: Optional[tuple[int]] = None, _circuit: Circuit = None)[source]#

Bases: QuantumEncoding

_set_phases(x: ndarray[Any, dtype[_ScalarType_co]])[source]#

Helper method to set the phases of the rotations of the internal circuit.

Parameters:: x (ndarray) – the input rotation angles.

__call__(x: ndarray[Any, dtype[_ScalarType_co]]) → Circuit[source]#

Construct the circuit encoding the x data in the rotation angles of some RY gates.

Parameters:: x (ndarray) – the input real data to encode in rotation angles.
Returns:: the constructed qibo.Circuit.
Return type:: (Circuit)

_abc_impl = <_abc._abc_data object>#

_circuit: Circuit = None#

property circuit: Circuit#: Internal initialized circuit.

property differentiable: bool#: Whether the encoder is differentiable. If True the gradient w.r.t. the inputs is calculated, otherwise it is automatically set to zero.

qubits: Optional[tuple[int]] = None#

nqubits: int#

class qiboml.models.encoding.BinaryEncoding(nqubits: int, qubits: Optional[tuple[int]] = None, _circuit: Circuit = None)[source]#

Bases: QuantumEncoding

__call__(x: ndarray[Any, dtype[_ScalarType_co]]) → Circuit[source]#

Construct the circuit encoding the x binary data in some RX rotation gates with angles either \(\pi\) (for ones) or 0 (for zeros).

Parameters:: x (ndarray) – the input binary data.
Returns:: the constructed qibo.Circuit.
Return type:: (Circuit)

property differentiable: bool#: Whether the encoder is differentiable. If True the gradient w.r.t. the inputs is calculated, otherwise it is automatically set to zero.

_abc_impl = <_abc._abc_data object>#

_circuit: Circuit = None#

property circuit: Circuit#: Internal initialized circuit.

qubits: Optional[tuple[int]] = None#

nqubits: int#

qiboml.models.ansatze#

qiboml.models.ansatze.ReuploadingCircuit(nqubits: int, qubits: list[int] = None, nlayers: int = 1) → Circuit[source]#

qiboml.models.decoding#

class qiboml.models.decoding.QuantumDecoding(nqubits: int, qubits: tuple[int] = None, nshots: int = None, backend: Backend = None, _circuit: Circuit = None)[source]#

Bases: object

Abstract decoder class.

Parameters:

nqubits (int) – total number of qubits.
qubits (tuple[int], optional) – set of qubits it acts on, by default range(nqubits).
nshots (int, optional) – number of shots used for circuit execution and sampling.
backend (Backend, optional) – backend used for computation, by default the globally-set backend is used.

nqubits: int#

qubits: tuple[int] = None#

nshots: int = None#

backend: Backend = None#

_circuit: Circuit = None#

__call__(x: Circuit) → Union[CircuitResult, QuantumState, MeasurementOutcomes][source]#

Combine the input circuir with the internal one and execute them with the internal backend.

Parameters:: x (Circuit) – input circuit.
Returns:: the execution qibo.result object.
Return type:: (CircuitResult | QuantumState | MeasurementOutcomes)

property circuit: Circuit#

A copy of the internal circuit.

Returns:: a copy of the internal circuit.
Return type:: (Circuit)

set_backend(backend: Backend)[source]#

Set the internal backend.

Parameters:: backend (Backend) – backend to be set.

property output_shape#: The shape of the decoded outputs.

property analytic: bool#

Whether the decoder is analytic, i.e. the gradient is ananlytically computable, or not (e.g. if sampling is involved).

Returns:: True if nshots is None, False otherwise.
Return type:: (bool)

class qiboml.models.decoding.Probabilities(nqubits: int, qubits: tuple[int] = None, nshots: int = None, backend: Backend = None, _circuit: Circuit = None)[source]#

Bases: QuantumDecoding

The probabilities decoder.

__call__(x: Circuit) → ndarray[Any, dtype[_ScalarType_co]][source]#

Computes the final state probabilities.

Parameters:: x (Circuit) – input circuit.
Returns:: the final probabilities.
Return type:: (ndarray)

property output_shape: tuple[int, int]#

Shape of the output probabilities.

Returns:: a (1, 2**nqubits) shape.
Return type:: (tuple[int, int])

property analytic: bool#

Whether the decoder is analytic, i.e. the gradient is ananlytically computable, or not (e.g. if sampling is involved).

Returns:: True if nshots is None, False otherwise.
Return type:: (bool)

_circuit: Circuit = None#

backend: Backend = None#

property circuit: Circuit#

A copy of the internal circuit.

Returns:: a copy of the internal circuit.
Return type:: (Circuit)

nshots: int = None#

qubits: tuple[int] = None#

set_backend(backend: Backend)#

Set the internal backend.

Parameters:: backend (Backend) – backend to be set.

nqubits: int#

class qiboml.models.decoding.Expectation(nqubits: int, qubits: tuple[int] = None, nshots: int = None, backend: Backend = None, _circuit: Circuit = None, observable: Union[ndarray[Any, dtype[_ScalarType_co]], Hamiltonian] = None)[source]#

Bases: QuantumDecoding

The expectation value decoder.

Parameters:: observable (Hamiltonian | ndarray) – the observable to calculate the expectation value of,

by default \(Z_0\otimes Z_1\otimes ... \otimes Z_n\) is used.

observable: Union[ndarray[Any, dtype[_ScalarType_co]], Hamiltonian] = None#

__call__(x: Circuit) → ndarray[Any, dtype[_ScalarType_co]][source]#

Execute the input circuit and calculate the expectation value of the internal observable on the final state

Parameters:: x (Circuit) – input Circuit.
Returns:: the calculated expectation value.
Return type:: (ndarray)

property output_shape: tuple[int, int]#

Shape of the output expectation value.

Returns:: a (1, 1) shape.
Return type:: (tuple[int, int])

set_backend(backend: Backend)[source]#

Set the internal and observable’s backends.

Parameters:: backend (Backend) – backend to be set.

_circuit: Circuit = None#

property analytic: bool#

Whether the decoder is analytic, i.e. the gradient is ananlytically computable, or not (e.g. if sampling is involved).

Returns:: True if nshots is None, False otherwise.
Return type:: (bool)

backend: Backend = None#

property circuit: Circuit#

A copy of the internal circuit.

Returns:: a copy of the internal circuit.
Return type:: (Circuit)

nshots: int = None#

qubits: tuple[int] = None#

nqubits: int#

class qiboml.models.decoding.State(nqubits: int, qubits: tuple[int] = None, nshots: int = None, backend: Backend = None, _circuit: Circuit = None)[source]#

Bases: QuantumDecoding

The state decoder.

__call__(x: Circuit) → ndarray[Any, dtype[_ScalarType_co]][source]#

Compute the final state of the input circuit and separates it in its real and imaginary parts stacked on top of each other.

Parameters:: x (Circuit) – input Circuit.
Returns:: the final state.
Return type:: (ndarray)

property output_shape: tuple[int, int, int]#

Shape of the output state.

Returns:: a (2, 1, 2**nqubits) shape.
Return type:: (tuple[int, int, int])

property analytic: bool#

Whether the decoder is analytic, i.e. the gradient is ananlytically computable, or not (e.g. if sampling is involved).

Returns:: True if nshots is None, False otherwise.
Return type:: (bool)

_circuit: Circuit = None#

backend: Backend = None#

property circuit: Circuit#

A copy of the internal circuit.

Returns:: a copy of the internal circuit.
Return type:: (Circuit)

nshots: int = None#

qubits: tuple[int] = None#

set_backend(backend: Backend)#

Set the internal backend.

Parameters:: backend (Backend) – backend to be set.

nqubits: int#

class qiboml.models.decoding.Samples(nqubits: int, qubits: tuple[int] = None, nshots: int = None, backend: Backend = None, _circuit: Circuit = None)[source]#

Bases: QuantumDecoding

The samples decoder.

__call__(x: Circuit) → ndarray[Any, dtype[_ScalarType_co]][source]#

Sample the final state of the circuit.

Parameters:: x (Circuit) – input Circuit.
Returns:: the generated samples.
Return type:: (ndarray)

property output_shape: tuple[int, int]#

Shape of the output samples.

Returns:: a (nshots, nqubits) shape.
Return type:: (tuple[int, int])

property analytic: bool#

Whether the decoder is analytic, i.e. the gradient is ananlytically computable, or not (e.g. if sampling is involved).

Returns:: True if nshots is None, False otherwise.
Return type:: (bool)

_circuit: Circuit = None#

backend: Backend = None#

property circuit: Circuit#

A copy of the internal circuit.

Returns:: a copy of the internal circuit.
Return type:: (Circuit)

nshots: int = None#

qubits: tuple[int] = None#

set_backend(backend: Backend)#

Set the internal backend.

Parameters:: backend (Backend) – backend to be set.

nqubits: int#