Source code for dimod.reference.composites.spin_transform

# Copyright 2018 D-Wave Systems Inc.
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
#    You may obtain a copy of the License at
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#    See the License for the specific language governing permissions and
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# ================================================================================================
On the D-Wave system, coupling :math:`J_{i,j}` adds a small bias to qubits :math:`i` and
:math:`j` due to leakage. This can become significant for chained qubits. Additionally,
qubits are biased to some small degree in one direction or another.
Applying a spin-reversal transform can improve results by reducing the impact of possible
analog and systematic errors. A spin-reversal transform does not alter the Ising problem;
the transform simply amounts to reinterpreting spin up as spin down, and visa-versa, for
a particular spin.
from random import random
import itertools

import numpy as np

from dimod.core.composite import Composite
from dimod.core.sampler import Sampler
from dimod.core.structured import Structured
from dimod.sampleset import SampleSet, concatenate
from dimod.vartypes import Vartype

__all__ = ['SpinReversalTransformComposite']

[docs]class SpinReversalTransformComposite(Sampler, Composite): """Composite for applying spin reversal transform preprocessing. Spin reversal transforms (or "gauge transformations") are applied by flipping the spin of variables in the Ising problem. After sampling the transformed Ising problem, the same bits are flipped in the resulting sample [#km]_. Args: sampler: A `dimod` sampler object. Examples: This example composes a dimod ExactSolver sampler with spin transforms then uses it to sample an Ising problem. >>> base_sampler = dimod.ExactSolver() >>> composed_sampler = dimod.SpinReversalTransformComposite(base_sampler) ... # Sample an Ising problem >>> response = composed_sampler.sample_ising({'a': -0.5, 'b': 1.0}, {('a', 'b'): -1}) >>> response.first.sample {'a': -1, 'b': -1} References ---------- .. [#km] Andrew D. King and Catherine C. McGeoch. Algorithm engineering for a quantum annealing platform., 2014. """ children = None parameters = None properties = None def __init__(self, child): self.children = [child] if isinstance(child, Structured): # todo something like Structured.__init__(self) raise NotImplementedError self.parameters = parameters = {'spin_reversal_variables': []} parameters.update(child.parameters) = {'child_properties':}
[docs] def sample(self, bqm, num_spin_reversal_transforms=2, **kwargs): """Sample from the binary quadratic model. Args: bqm (:obj:`~dimod.BinaryQuadraticModel`): Binary quadratic model to be sampled from. num_spin_reversal_transforms (integer, optional, default=2): Number of spin reversal transform runs. Returns: :obj:`.SampleSet` Examples: This example runs 100 spin reversals applied to one variable of a QUBO problem. >>> base_sampler = dimod.ExactSolver() >>> composed_sampler = dimod.SpinReversalTransformComposite(base_sampler) ... >>> Q = {('a', 'a'): -1, ('b', 'b'): -1, ('a', 'b'): 2} >>> response = composed_sampler.sample_qubo(Q, ... num_spin_reversal_transforms=100) >>> len(response) 400 """ # make a main response responses = [] flipped_bqm = bqm.copy() transform = {v: False for v in bqm.variables} for ii in range(num_spin_reversal_transforms): # flip each variable with a 50% chance for v in bqm: if random() > .5: transform[v] = not transform[v] flipped_bqm.flip_variable(v) flipped_response = self.child.sample(flipped_bqm, **kwargs) tf_idxs = [flipped_response.variables.index(v) for v, flip in transform.items() if flip] if bqm.vartype is Vartype.SPIN: flipped_response.record.sample[:, tf_idxs] = -1 * flipped_response.record.sample[:, tf_idxs] else: flipped_response.record.sample[:, tf_idxs] = 1 - flipped_response.record.sample[:, tf_idxs] responses.append(flipped_response) return concatenate(responses)