scikit_quri.circuit package

Submodules

scikit_quri.circuit.circuit module

scikit_quri.circuit.circuit.need_learning_parameter_guard(func, companion_parameter_id)

Parameters:

• func (Union[Callable[[ndarray[tuple[int, ...], dtype[float64]]], float], Callable[[float, ndarray[tuple[int, ...], dtype[float64]]], float]]) –

• companion_parameter_id (Optional[int]) –

Return type:

TypeGuard[Callable[[ndarray[tuple[int, …], dtype[float64]]], float]]

scikit_quri.circuit.circuit.not_needed_learning_parameter_guard(func, companion_parameter_id)

Parameters:

• func (Union[Callable[[ndarray[tuple[int, ...], dtype[float64]]], float], Callable[[float, ndarray[tuple[int, ...], dtype[float64]]], float]]) –

• companion_parameter_id (Optional[int]) –

Return type:

TypeGuard[Callable[[float, ndarray[tuple[int, …], dtype[float64]]], float]]

class scikit_quri.circuit.circuit.LearningCircuit(n_qubits: int)

Bases: object

Parameters:

n_qubits (int) –

n_qubits: int

circuit: ParametricQuantumCircuit

input_functions: dict[int, Callable[[numpy.ndarray[tuple[int, ...], numpy.dtype[numpy.float64]]], float]]

add_gate(gate)

Add arbitrary gate.

Parameters:

gate (QuantumGate) – Gate to add.

Return type:

None

add_X_gate(index)

Parameters:

index (int) – Index of qubit to add X gate.

Return type:

None

add_Y_gate(index)

Parameters:

index (int) – Index of qubit to add Y gate.

Return type:

None

add_Z_gate(index)

Parameters:

index (int) – Index of qubit to add Z gate.

Return type:

None

add_RX_gate(index, angle)

Parameters:

• index (int) – Index of qubit to add RX gate.

• angle (float) – Rotation angle.

Return type:

None

add_RY_gate(index, parameter)

Parameters:

• index (int) – Index of qubit to add RY gate.

• angle – Rotation angle.

• parameter (float) –

Return type:

None

add_RZ_gate(index, parameter)

Parameters:

• index (int) – Index of qubit to add RZ gate.

• angle – Rotation angle.

• parameter (float) –

Return type:

None

add_CNOT_gate(control_index, target_index)

Parameters:

• control_index (int) – Index of control qubit.

• target_index (int) – Index of target qubit.

Return type:

None

add_H_gate(index)

Parameters:

index (int) – Index of qubit to put H gate.

Return type:

None

add_input_RX_gate(qubit, input_function)

Parameters:

• qubit (int) –

• input_function (Callable[[ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

add_input_RY_gate(qubit, input_function)

Parameters:

• qubit (int) –

• input_function (Callable[[ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

add_input_RZ_gate(qubit, input_function)

Parameters:

• qubit (int) –

• input_function (Callable[[ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

add_parametric_RX_gate(qubit, share_with=None, share_with_coef=None)

Parameters:

• qubit (int) –

• share_with (Optional[int]) –

• share_with_coef (Optional[float]) –

Return type:

int

add_parametric_RY_gate(qubit, share_with=None, share_with_coef=None)

Parameters:

• qubit (int) –

• share_with (Optional[int]) –

• share_with_coef (Optional[float]) –

Return type:

int

add_parametric_RZ_gate(qubit, share_with=None, share_with_coef=None)

Parameters:

• qubit (int) –

• share_with (Optional[int]) –

• share_with_coef (Optional[float]) –

Return type:

int

add_parametric_multi_Pauli_rotation_gate(targets, pauli_ids)

Parameters:

• targets (List[int]) –

• pauli_ids (List[int]) –

Return type:

Parameter

add_parametric_input_RX_gate(index, input_func=<function LearningCircuit.<lambda>>)

Parameters:

• index (int) –

• input_func (Callable[[float, ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

add_parametric_input_RY_gate(index, input_func=<function LearningCircuit.<lambda>>)

Parameters:

• index (int) –

• input_func (Callable[[float, ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

add_parametric_input_RZ_gate(index, input_func=<function LearningCircuit.<lambda>>)

Parameters:

• index (int) –

• input_func (Callable[[float, ndarray[tuple[int, ...], dtype[float64]]], float]) –

Return type:

None

property parameter_count: int

property input_params_count: int

property learning_params_count: int

get_learning_param_indexes()

Return type:

List[int]

get_minimum_learning_param_indexes()

Circuit内のパラメータのうち，Circuitを構成できる最小のパラメータのインデックスを返す

Return type:

List[int]

get_input_params_indexes()

Return type:

List[int]

bind_input_and_parameters(x, parameters)

Parameters:

• x (ndarray[tuple[int, ...], dtype[float64]]) –

• parameters (ndarray[tuple[int, ...], dtype[float64]]) –

Return type:

ImmutableBoundParametricQuantumCircuit

generate_bound_params(x, parameters)

x: Input data, theta: Learning parametersから，Circuitにbindするパラーメータを生成する

Parameters:

• x (ndarray[tuple[int, ...], dtype[float64]]) –

• parameters (ndarray[tuple[int, ...], dtype[float64]]) –

Return type:

Sequence[float]

backprop_innner_product(x, theta, state)

backprop(self, x: List[float], state)->List[Float] qulacsに回路を変換しinner_productでbackpropします。

Parameters:

• x (ndarray[tuple[int, ...], dtype[float64]]) –

• theta (ndarray[tuple[int, ...], dtype[float64]]) –

• state (QuantumState) –

Return type:

ndarray[tuple[int, …], dtype[float64]]

to_batched(data, parameters)

scaluq(quri-parts-scaluq)に流すためのMethod data: (n_data, n_features) theta: (n_params)

Returns:

(UnboundParametricQuantumCircuit, NDArray[n_data, params])

Return type:

(circuit, batched_params)

Parameters:

• data (ndarray[tuple[int, ...], dtype[float64]]) –

• parameters (ndarray[tuple[int, ...], dtype[float64]]) –

scikit_quri.circuit.circuit.preprocess_x(x, i)

Parameters:

• x (ndarray[tuple[int, ...], dtype[float64]]) –

• i (int) –

Return type:

float

scikit_quri.circuit.pre_defined module

scikit_quri.circuit.pre_defined.CZ()

scikit_quri.circuit.pre_defined.CNOT()

scikit_quri.circuit.pre_defined.create_qcl_ansatz(n_qubit, c_depth, time_step=0.5, seed=0)

Create a circuit used in this page: https://dojo.qulacs.org/ja/latest/notebooks/5.2_Quantum_Circuit_Learning.html

Parameters:

• n_qubit (int) – number of qubits

• c_depth (int) – circuit depth

• time_step (float) – the evolution time used for the hamiltonian dynamics

• seed (Optional[int]) – seed for random numbers. used for determining the interaction strength of the hamiltonian simulation

Return type:

LearningCircuit

Examples

>>> n_qubit = 4
>>> circuit = create_qcl_ansatz(n_qubit, 3, 0.5)
>>> qnn = QNNRegressor(circuit)
>>> qnn.fit(x_train, y_train)

scikit_quri.circuit.pre_defined.preprocess_x(x, i)

Parameters:

• x (ndarray) –

• i (int) –

Return type:

float

scikit_quri.circuit.pre_defined.create_farhi_neven_ansatz(n_qubit, c_depth, seed=0)

Parameters:

• n_qubit (int) –

• c_depth (int) –

• seed (Optional[int]) –

Return type:

LearningCircuit

scikit_quri.circuit.pre_defined.create_ibm_embedding_circuit(n_qubit)

create circuit proposed in https://arxiv.org/abs/1802.06002.

Parameters:

• n_qubits – number of qubits

• n_qubit (int) –

Return type:

LearningCircuit

scikit_quri.circuit.pre_defined.create_dqn_cl(n_qubit, c_depth, s_qubit)

Parameters:

• n_qubit (int) –

• c_depth (int) –

• s_qubit (int) –

Return type:

LearningCircuit

scikit_quri.circuit.pre_defined.create_dqn_cl_no_cz(n_qubit, c_depth)

Parameters:

• n_qubit (int) –

• c_depth (int) –

Return type:

LearningCircuit

scikit_quri.circuit.pre_defined.create_qcnn_ansatz(n_qubit, seed=0)

Creates circuit used in https://www.tensorflow.org/quantum/tutorials/qcnn?hl=en, Section 1.

Parameters:

• n_qubit (int) – number of qubits. must be even.

• seed (Optional[int]) – seed for random numbers. used for determining the interaction strength of the hamiltonian simulation

Return type:

LearningCircuit

Module contents