qat.opt.GraphPartitioning

class qat.opt.GraphPartitioning(graph, A, B=1, **kwargs)

Specialization of the Ising class for Graph Partitioning.

For right encoding we need \(\frac { A } { B } \geq \frac { min(2D, N) } { 8 }\) with \(D\) - the maximal degree of a node in the graph and \(N\) - the number of nodes.

Reference

“Ising formulations of many NP problems”, A. Lucas, 2014 - Section 2.2.

import numpy as np
import networkx as nx
from qat.opt import GraphPartitioning

graph = nx.Graph()
graph.add_nodes_from(np.arange(10))
graph.add_edges_from([(0,1), (0,4), (0,6), (1,2), (1,4), (1,7), (2,3), (2,5), (2,8),
                      (3,5), (3,9), (4,6), (4,7), (5,8), (5,9), (6,7), (7,8), (8,9)])

B = 2
A = 5

graph_partitioning_problem = GraphPartitioning(graph, A, B=B)

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

To anneal the problem, the solver would need 10 spins.
Parameters

  • graph (networkx.Graph) – a networkx graph

  • A (double) – a positive constant by which the terms inside \(H_A\) from \(H = H_A + H_B\) are multiplied. This equation comes from the Hamiltonian representation of the problem.

  • B (optional, double) – similar to \(A\), \(B\) is a positive factor for the \(H_B\) terms, default is 1

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 Graph Partitioning problem from a list of samples

Parameters

result (BatchResult) – BatchResult containing a list of samples

Returns

The best balanced partition among the samples with the minimum cut size

Return type

GraphPartitioningResult

qat.opt.graph_partitioning.produce_j_h_and_offset(graph, A, B=1)

Returns the \(J\) coupling matrix of the problem, along with the magnetic field \(h\) and the Ising energy offset. For right encoding we need \(\frac{A}{B} \geq \frac{min(2D, N)}{8}\) with \(D\) - the maximal degree of a node in the graph and \(N\) - the number of nodes.

Parameters

  • graph (networkx.Graph) – a networkx graph

  • A (double) – a positive constant by which the terms inside \(H_A\) from \(H = H_A + H_B\) are multiplied. This equation comes from the Hamiltonian representation of the problem.

  • B (optional, double) – similar to \(A\), \(B\) is a positive factor for the \(H_B\) terms, default is 1