qat.opt.VertexCover

class qat.opt.VertexCover(graph, A=2, B=1, **kwargs)

Specialization of the QUBO class for Vertex Cover.

For a right encoding, one should ensure that \(A > B\).

Reference

“Ising formulations of many NP problems”, A. Lucas, 2014 - Section 4.3.

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

graph = nx.Graph()
graph.add_nodes_from(np.arange(6))
graph.add_edges_from([(0,1), (0,2), (0,3), (0,4), (0,5), (1,5)])
A = 2
B = 1

vertex_cover_problem = VertexCover(graph, A=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 6 spins.
Parameters

  • graph (networkx.Graph) – a networkx graph

  • A (optional, double) – a positive constant by which the terms inside \(H_A\) from \(H = H_A + H_B\) are multiplied, default is 2. 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 approximated solution of the Vertex Cover problem from a list of samples

Parameters

result (BatchResult) – BatchResult containing a list of samples

qat.opt.vertex_cover.produce_q_and_offset(graph, A=2, B=1)

Returns the \(Q\) matrix and the offset energy of the problem. The constant \(A\) should be bigger than \(B\) for a right encoding. They are also both positive.

Parameters

  • graph (networkx.Graph) – a networkx graph

  • A (optional, double) – a positive constant by which the terms inside \(H_A\) from \(H = H_A + H_B\) are multiplied, default is 2. 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