kaiwu.sampler package#

Module contents#

Module: sampler

Function: Provide a series of data post-processing tools

class kaiwu.sampler.SimulatedAnnealingSampler(initial_temperature=100, alpha=0.99, cutoff_temperature=0.001, iterations_per_t=10, size_limit=100, flag_evolution_history=False, verbose=False, rand_seed=None, process_num=1)#

Bases: SimulatedAnnealingSamplerBase

Simulated annealing sampler for solving Ising models. Each solution is solved independently

Parameters:

  • initial_temperature (float) – Initial temperature.

  • alpha (float) – Temperature reduction coefficient.

  • cutoff_temperature (float) – Cutoff temperature.

  • iterations_per_t (int) – Iteration depth per temperature.

  • size_limit (int) – Number of output solutions; defaults to 100 solutions

  • flag_evolution_history (bool) – Whether to output the Hamiltonian evolution history; defaults to False. When True, the evolution history can be obtained via the get_ha_history method

  • verbose (bool) – Whether to output calculation progress in the console; defaults to False

  • rand_seed (int, optional) – Random seed for the numpy random number generator

  • process_num (int, optional) – Number of parallel processes (-1 automatically uses all available cores, 1 for single process). Defaults to 1.

Examples

>>> import numpy as np
>>> import kaiwu as kw
>>> matrix = -np.array([[ 0. ,  1. ,  0. ,  1. ,  1. ],
...                     [ 1. ,  0. ,  0. ,  1.,   1. ],
...                     [ 0. ,  0. ,  0. ,  1.,   1. ],
...                     [ 1. ,  1.,   1. ,  0. ,  1. ],
...                     [ 1. ,  1.,   1. ,  1. ,  0. ]])
>>> worker = kw.classical.SimulatedAnnealingOptimizer(initial_temperature=100,
...                                                   alpha=0.99,
...                                                   cutoff_temperature=0.001,
...                                                   iterations_per_t=10,
...                                                   size_limit=10)
>>> # This value is random and cannot be predicted
>>> worker.solve(matrix) 
array([[-1,  1, -1,  1,  1],
       [-1,  1,  1, -1,  1],
       [-1,  1,  1, -1, -1],
       [ 1, -1, -1,  1,  1],
       [ 1, -1,  1, -1,  1],
       [ 1, -1,  1, -1, -1],
       [ 1, -1, -1, -1,  1],
       [ 1, -1,  1,  1, -1],
       [ 1,  1,  1, -1, -1],
       [-1, -1, -1,  1,  1]])
on_matrix_change()#

Update matrix-related information

get_ha_history()#

Get Hamiltonian evolution history

Returns:

Hamiltonian evolution history over time, where keys are time in seconds and values are Hamiltonian values

Return type:

dict

single_process_solve(ising_matrix=None, init_solution=None, rand_seed=None, size_limit=None)#

Solve Ising matrix in a single process

Parameters:

  • ising_matrix (np.ndarray, optional) – Ising matrix. Defaults to None.

  • init_solution (np.ndarray, optional) – Initial solution vector. Defaults to None.

  • rand_seed (int, optional) – Random seed for the numpy random number generator

Returns:

solution vector

Return type:

np.ndarray

get_hamiltonian()#
Returns:

Hamiltonian value of the current solution

Return type:

hamiltonian (np.ndarray)

set_matrix(ising_matrix)#

Set matrix and update related content

solve(ising_matrix=None, negtail_flip=True, sort_solutions=False)#

Solve the Ising matrix

Parameters:

  • ising_matrix (np.ndarray) – Isingmatrix

  • negtail_flip (bool) – Whether to perform negative-tail flipping

  • sort_solutions (bool) – Whether to sort solutions

Returns:

solution vector

Return type:

output (np.ndarray)

solve_qubo(*args, **kwargs)#
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