D-Wave PyTorch Plugin

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.

Example

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, crayon=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))

License

Released under the Apache License 2.0. See LICENSE file.

Contributing

Ocean's contributing guide has guidelines for contributing to Ocean packages.

Release Notes

dwave-pytorch-plugin uses reno to manage its release notes.

When making a contribution to dwave-pytorch-plugin that will affect users, create a new release note file by running

reno new your-short-descriptor-here

You can then edit the file created under releasenotes/notes/. Remove any sections not relevant to your changes. Commit the file along with your changes.

See reno's user guide for details.