mindquantum.algorithm.qaia.ASB

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mindquantum.algorithm.qaia.ASB#

class mindquantum.algorithm.qaia.ASB(J, h=None, x=None, n_iter=1000, batch_size=1, dt=1, xi=None, M=2, backend='cpu-float32')#

Adiabatic SB algorithm.

Reference: Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems.

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 [-0.01, 0.01]. 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.

  • xi (float) – positive constant with the dimension of frequency. Default: None.

  • M (int) – The number of update without mean-field terms. Default: 2.

  • backend (str) – Computation backend and precision to use: ‘cpu-float32’, ‘gpu-float32’,’npu-float32’. Default: 'cpu-float32'.

Examples

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

Dynamical evolution based on Modified explicit symplectic Euler method.

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