mindquantum.core.gates.gene_univ_parameterized_gate

mindquantum.core.gates.gene_univ_parameterized_gate(name, matrix_generator, diff_matrix_generator)

Generate a customer parameterized gate based on the single parameter defined unitary matrix.

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 or a method that take exactly one argument to generate a unitary matrix.

• diff_matrix_generator (Union[FunctionType, MethodType]) – A function or a method that take exactly one argument to generate the derivative of this unitary matrix.

Returns

_ParamNonHerm, a customer parameterized gate.

Examples

>>> import numpy as np
>>> from mindquantum.core.circuit import Circuit
>>> from mindquantum.core.gates import gene_univ_parameterized_gate
>>> from mindquantum.simulator import Simulator
>>> def matrix(alpha):
...     ep = 0.5 * (1 + np.exp(1j * np.pi * alpha))
...     em = 0.5 * (1 - np.exp(1j * np.pi * alpha))
...     return np.array([
...         [1.0 + 0.0j, 0.0j, 0.0j, 0.0j],
...         [0.0j, ep, em, 0.0j],
...         [0.0j, em, ep, 0.0j],
...         [0.0j, 0.0j, 0.0j, 1.0 + 0.0j],
...     ])
>>> def diff_matrix(alpha):
...     ep = 0.5 * 1j * np.pi * np.exp(1j * np.pi * alpha)
...     em = -0.5 * 1j * np.pi * np.exp(1j * np.pi * alpha)
...     return np.array([
...         [0.0j, 0.0j, 0.0j, 0.0j],
...         [0.0j, ep, em, 0.0j],
...         [0.0j, em, ep, 0.0j],
...         [0.0j, 0.0j, 0.0j, 0.0j],
...     ])
>>> TestGate = gene_univ_parameterized_gate('Test', matrix, diff_matrix)
>>> circ = Circuit().h(0)
>>> circ += TestGate('a').on([0, 1])
>>> circ
q0: ──H────Test(a)──
              │
q1: ───────Test(a)──
>>> circ.get_qs(pr={'a': 1.2})
array([0.70710678+0.j        , 0.06752269-0.20781347j,
       0.63958409+0.20781347j, 0.        +0.j        ])