mindquantum.core.gates.gene_univ_two_params_gate

mindquantum.core.gates.gene_univ_two_params_gate(name, matrix_generator, diff_matrix_generator_1, diff_matrix_generator_2)

Generate a customer parameterized gate with two parameters.

Note

The elements in the matrix need to be explicitly written in complex number form, especially for multi qubits gate.

Parameters

• name (str) – The name of this gate.

• matrix_generator (Union[FunctionType, MethodType]) – A function that takes two arguments to generate a unitary matrix.

• diff_matrix_generator_1 (Union[FunctionType, MethodType]) – A function that takes two arguments to generate the derivative of matrix with respect to first parameter.

• diff_matrix_generator_2 (Union[FunctionType, MethodType]) – A function that takes two arguments to generate the derivative of matrix with respect to second parameter.

Returns

_TwoParamNonHerm, a customer parameterized gate with two parameters.

Examples

>>> import numpy as np
>>> from mindquantum.core.gates import gene_univ_two_params_gate
>>> from mindquantum.core.circuit import Circuit
>>> def matrix(a, b):
...     ep = 0.5 * (1 + np.exp(1j * a * b))
...     em = 0.5 * (1 - np.exp(1j * a * b))
...     return np.array([[ep, em], [em, ep]])
>>> def diff_matrix1(a, b):
...     m = 0.5j * b * np.exp(1j * a * b)
...     return np.array([[m, -m], [-m, m]])
>>> def diff_matrix2(a, b):
...     m = 0.5j * a * np.exp(1j * a * b)
...     return np.array([[m, -m], [-m, m]])
>>> TestGate = gene_univ_two_params_gate('Test', matrix, diff_matrix1, diff_matrix2)
>>> circ = Circuit().x(0)
>>> circ += TestGate('a', 'b').on(0)
>>> circ.get_qs(pr={'a': 1.2, 'b': 0.8})
array([0.21324001-0.40959578j, 0.78675999+0.40959578j])