This plugin provides an interface between D-Wave's quantum computers and the PyTorch framework, including neural network modules for building and training Boltzmann Machines along with various sampler utility functions.
Boltzmann Machines are probabilistic generative models for high-dimensional binary data. The following example walks through a typical workflow for fitting Boltzmann Machines via maximum likelihood.
Define a Graph-Restricted Boltzmann Machine with a square graph
import torch
from torch.optim import SGD
from dwave.plugins.torch.models import GraphRestrictedBoltzmannMachine as GRBM
from dwave.plugins.torch.samplers import BlockSampler
grbm = GRBM(nodes=["a", "b", "c", "d"], edges=[("a", "b"), ("b", "c"), ("c", "d"), ("d", "a")])
print("Linear weights:", grbm.linear)
print("Quadratic weights:", grbm.quadratic)Instantiate a block-Gibbs sampler. Variables "a" and "c" are in block 0; variables "b" and "d" are in block 1. The sampler consists of three parallel Markov chains of length ten each. Each Markov chain samples at a constant unit inverse temperature.
sampler = BlockSampler(grbm=grbm, colouring=lambda v: v in {"b", "d"}, num_chains=3, schedule=[1]*10)Create a batch of data and perform one likelihood-optimization step
x_data = torch.tensor([[1, -1, 1, -1], [-1, 1, 1, 1]], dtype=torch.float32)
optimizer = SGD(grbm.parameters(), lr=1)
x_model = sampler.sample()
grbm.quasi_objective(x_data, x_model).backward()
optimizer.step()
print("Updated quadratic weights:", grbm.quadratic)To use a dimod sampler, replace the sampler = BlockSampler(...) line with
from dwave.plugins.torch.samplers import DimodSampler
from dwave.samplers import RandomSampler
sampler = DimodSampler(grbm=grbm, sampler=RandomSampler(),
prefactor=1, sample_kwargs=dict(num_reads=5))Released under the Apache License 2.0. See LICENSE file.