mindquantum.algorithm.qaia.LQA
- class mindquantum.algorithm.qaia.LQA(J, h=None, x=None, n_iter=1000, batch_size=1, gamma=0.1, dt=1.0, momentum=0.99)[source]
Local quantum annealing algorithm.
Reference: Quadratic Unconstrained Binary Optimization via Quantum-Inspired Annealing.
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 random values uniformly distributed in [-0.1, 0.1]. Default:None.n_iter (int) – The number of iterations. Default:
1000.batch_size (int) – The number of sampling. Default:
1.dt (float) – The step size. Default:
1.gamma (float) – The coupling strength. Default:
0.1.momentum (float) – Momentum factor. Default:
0.99.
Examples
>>> import numpy as np >>> from mindquantum.algorithm.qaia import LQA >>> J = np.array([[0, -1], [-1, 0]]) >>> solver = LQA(J, batch_size=5) >>> solver.update() >>> print(solver.calc_cut()) [1. 1. 1. 1. 1.] >>> print(solver.calc_energy()) [-1. -1. -1. -1. -1.]