mindquantum.algorithm.qaia.NMFA

class mindquantum.algorithm.qaia.NMFA(J, h=None, x=None, n_iter=1000, batch_size=1, alpha=0.15, sigma=0.15)

Noisy Mean-field Annealing algorithm.

Reference: Emulating the coherent Ising machine with a mean-field algorithm.

Note

For memory efficiency, the input array 'x' is not copied and will be modified in-place during optimization. If you need to preserve the original data, please pass a copy using x.copy().

Parameters

• J (Union[numpy.array, scipy.sparse.spmatrix]) – The coupling matrix with shape \((N x N)\).

• h (numpy.array) – The external field with shape \((N, )\).

• x (numpy.array) – The initialized spin value with shape \((N x batch_size)\). Will be modified during optimization. If not provided (None), will be initialized as zeros. Default: None.

• n_iter (int) – The number of iterations. Default: 1000.

• batch_size (int) – The number of sampling. Default: 1.

• alpha (float) – Momentum factor. Default: 0.15.

• sigma (float) – The standard deviation of noise. Default: 0.15.

Examples

>>> import numpy as np
>>> from mindquantum.algorithm.qaia import NMFA
>>> J = np.array([[0, -1], [-1, 0]])
>>> solver = NMFA(J, batch_size=5)
>>> solver.update()
>>> print(solver.calc_cut())
[1. 1. 1. 1. 1.]
>>> print(solver.calc_energy())
[-1. -1. -1. -1. -1.]

initialize()

Initialize spin values.

update()

Dynamical evolution.