qat.opt.MaxCut

class qat.opt.MaxCut(graph, **kwargs)

Specialization of the Ising class for Max Cut.

Reference

Maximum cut, Theoretical physics, Wikipedia.

import networkx as nx
from qat.opt import MaxCut

graph = nx.full_rary_tree(2, 2**8)

maxcut = MaxCut(graph)

print("To anneal the problem, the solver would need "
      + str(len(graph.nodes())) + " spins.")

To anneal the problem, the solver would need 256 spins.
Parameters

graph (networkx.Graph) – a networkx graph

get_best_parameters()
Returns

6-key dictionary containing

  • n_monte_carlo_updates (int) - the number of Monte Carlo updates

  • n_trotters (int) - the number of “classical replicas” or “Trotter replicas”

  • gamma_max (double) - the starting magnetic field

  • gamma_min (double) - the final magnetic field

  • temp_max (double) - the starting temperature

  • temp_min (double) - the final temperature

parse_result(result, inverse=False)

Returns the best approximated solution of the Max Cut problem from a list of samples

Parameters

result (BatchResult) – BatchResult containing a list of samples

Returns

The best partition among the samples with the maximum cut size

Return type

GraphPartitioningResult

qat.opt.max_cut.produce_j_h_and_offset(graph)

Returns the \(J\) coupling matrix of the problem, along with the magnetic field \(h\) and the Ising energy offset.

Parameters

graph (networkx.Graph) – a networkx graph