Note: This is an alpha release of this package.

dwave-system

dwave-system is a basic API for easily incorporating the D-Wave system as a sampler in the D-Wave Ocean software stack. It includes DWaveSampler, a dimod.Sampler that accepts and passes system parameters such as system identification and authentication down the stack. It also includes several useful composites—layers of pre- and post-processing—that can be used with DWaveSampler to handle minor-embedding, optimize chain strength, etc.

Documentation

Release:0.2.3
Date:Apr 02, 2018

Reference Documentation

Release:0.2.3
Date:Apr 02, 2018

Samplers

Samplers are processes that sample from low energy states of a problem’s objective function. A binary quadratic model (BQM) sampler samples from low energy states in models such as those defined by an Ising equation or a Quadratic Unconstrained Binary Optimization (QUBO) problem and returns an iterable of samples, in order of increasing energy. A dimod sampler provides ‘sample_qubo’ and ‘sample_ising’ methods as well as the generic BQM sampler method.

dwave-system provides dimod samplers for using the D-Wave system.

Release:0.2.3
Date:Apr 02, 2018
D-Wave Sampler

<<<<<<< HEAD Class =====

Overview
class DWaveSampler(config_file=None, profile=None, endpoint=None, token=None, solver=None, proxy=None, permissive_ssl=False)[source]

A class for using the D-Wave system as a sampler.

Inherits from dimod.Sampler and dimod.Structured.

Enables quick incorporation of the D-Wave system as a sampler in the D-Wave Ocean software stack. Also enables optional customizing of input parameters to D-Wave Cloud Client (the stack’s communication-manager package).

Parameters:
  • config_file (str, optional) – Path to a D-Wave Cloud Client configuration file that identifies a D-Wave system and provides connection information.
  • profile (str, optional) – Profile to select from a D-Wave Cloud Client configuration file.
  • endpoint (str, optional) – D-Wave API endpoint URL. If specified, used instead of retrieving a value from a D-Wave Cloud Client configuration file.
  • token (str, optional) – Authentication token for the D-Wave API to authenticate the client session. If specified, used instead of retrieving a value from a D-Wave Cloud Client configuration file.
  • solver (str, optional) – Solver (a D-Wave system on which to run submitted problems). If specified, used instead of retrieving a value from a D-Wave Cloud Client configuration file.
  • proxy (str, optional) – Proxy URL to be used for accessing the D-Wave API. If specified, used instead of retrieving a value from a D-Wave Cloud Client configuration file.

Examples

This example creates a DWaveSampler based on a fictive user’s D-Wave Cloud Client configuration file and submits a simple Ising problem of just two variables that map to qubits 0 and 1 on the example system. (The simplicity of this example obviates the need for an embedding composite—the presence of qubits 0 and 1 on the selected D-Wave system can be verified manually.)

>>> # Example configuration file /home/susan/.config/dwave/dwave.conf:
>>> #    [defaults]
>>> #    endpoint = https://url.of.some.dwavesystem.com/sapi
>>> #    client = qpu
>>> #
>>> #    [dw2000]
>>> #    solver = EXAMPLE_2000Q_SYSTEM
>>> #    token = ABC-123456789123456789123456789
>>> from dwave.system.samplers import DWaveSampler
>>> sampler = DWaveSampler('/home/susan/.config/dwave/dwave.conf')
>>> response = sampler.sample_ising({0: -1, 1: 1}, {})
>>> for sample in response.samples():  
...    print(sample)
...
{0: 1, 1: -1}
Sampler Properties
DWaveSampler.properties dict – D-Wave solver properties as returned by a SAPI query.
DWaveSampler.parameters dict[str, list] – D-Wave solver parameters in the form of a dict, where keys are
Structured Sampler Properties
DWaveSampler.nodelist list – List of active qubits for the D-Wave solver.
DWaveSampler.edgelist list – List of active couplers for the D-Wave solver.
DWaveSampler.adjacency dict[variable, set] – The adjacency structure.
DWaveSampler.structure A namedtuple Structure(nodelist, edgelist, adjacency)
Methods
DWaveSampler.sample(bqm, **parameters) Samples from the given bqm using the instantiated sample method.
DWaveSampler.sample_ising(h, J, **kwargs) Sample from the provided Ising model.
DWaveSampler.sample_qubo(Q, **kwargs) Sample from the provided QUBO.

Composites

Samplers can be composed. The composite pattern allows layers of pre- and post-processing to be applied to binary quadratic programs without needing to change the underlying sampler implementation.

We refer to these layers as composites. A composed sampler includes at least one sampler and possibly many composites.

dwave-system provides dimod composites for using the D-Wave system.

For example, the D-Wave system is Chimera-structured (a particular architecture of sparsely connected qubits) and so any arbitrarily posed binary quadratic problem requires mapping, called minor embedding, to a Chimera graph that represents the system’s quantum processing unit. This preprocessing can be done by a composed sampler consisting of the DWaveSampler and a composite that performs minor-embedding.

Release:0.2.3
Date:Apr 02, 2018
EmbeddingComposite
Class

Because the D-Wave System is Chimera-structured but most problems of application interest are not, it is convenient to be able to map from a structured sampler to an unstructured one.

A structured sampler is one that can only solver problems that map to a specific graph (see structured)

The EmbeddingComposite uses the minorminer library to map unstructured problems to a structured sampler.

class EmbeddingComposite(child_sampler)[source]

Composite to map unstructured problems to a structured sampler.

Parameters:sampler (dimod.Sampler) – A structured dimod sampler.
Sampler Properties
EmbeddingComposite.properties dict – Contains one key 'child_properties' which has a copy of the child sampler’s properties.
EmbeddingComposite.parameters dict[str, list] – The keys are the keyword parameters accepted by the child sampler.
Composite Properties
EmbeddingComposite.children list – Contains the single wrapped structured sampler.
EmbeddingComposite.child The first child in children.
Methods
EmbeddingComposite.sample(bqm, **parameters) Samples from the given bqm using the instantiated sample method.
EmbeddingComposite.sample_ising(h, J, …) Sample from the provided unstructured Ising model.
EmbeddingComposite.sample_qubo(Q, **parameters) Samples from the given QUBO using the instantiated sample method.
TilingComposite
Class

Tiles many smaller problems across a larger Chimera-structured sampler.

class TilingComposite(sampler, sub_m, sub_n, t=4)[source]

Composite to tile a small problem across a Chimera-structured sampler. A problem that can fit on a small Chimera graph can be replicated across a larger Chimera graph to get samples from multiple areas of the system in one call. For example, a 2x2 Chimera lattice could be tiled 64 times (8x8) on a fully-yielded D-Wave 2000Q system (16x16).

Parameters:
  • sampler (dimod.Sampler) – A structured dimod sampler to be wrapped.
  • sub_m (int) – The number of rows in the sub-Chimera lattice.
  • sub_n (int) – The number of columns in the sub-Chimera lattice.
  • t (int) – The size of the shore within each Chimera cell.
Sampler Properties
TilingComposite.properties dict – Contains one key 'child_properties' which has a copy of the child sampler’s properties.
TilingComposite.parameters dict[str, list] – The keys are the keyword parameters accepted by the child sampler.
Composite Properties
TilingComposite.children list – Contains the single wrapped structured sampler.
TilingComposite.child The first child in children.
Structure Properties
TilingComposite.nodelist list – The nodes available to the sampler.
TilingComposite.edgelist list – The edges available to the sampler.
TilingComposite.adjacency dict[variable, set] – The adjacency structure.
TilingComposite.structure A namedtuple Structure(nodelist, edgelist, adjacency)
Methods
TilingComposite.sample(bqm, **parameters) Samples from the given bqm using the instantiated sample method.
TilingComposite.sample_ising(h, J, **kwargs) Sample from the sub-Chimera lattice.
TilingComposite.sample_qubo(Q, **parameters) Samples from the given QUBO using the instantiated sample method.
VirtualGraphComposite
Class

The D-Wave virtual graph tools simplify the process of minor-embedding by enabling you to more easily create, optimize, use, and reuse an embedding for a given working graph. When you submit an embedding and specify a chain strength using these tools, they automatically calibrate the qubits in a chain to compensate for the effects of biases that may be introduced as a result of strong couplings.

class VirtualGraphComposite(sampler, embedding, chain_strength=None, flux_biases=None, flux_bias_num_reads=1000, flux_bias_max_age=3600)[source]

Apply the VirtualGraph composite layer to the given solver.

Parameters:
  • sampler (DWaveSampler) – A dimod dimod.Sampler. Normally DWaveSampler, or a derived composite sampler. Other samplers in general will not work or will not make sense with this composite layer.
  • embedding (dict[hashable, iterable]) – A mapping from a source graph to the given sampler’s graph (the target graph).
  • chain_strength (float, optional, default=None) – The desired chain strength. If None, will use the maximum available from the processor.
  • flux_biases (list/False/None, optional, default=None) – The per-qubit flux bias offsets. If given, should be a list of lists. Each sublist should be of length 2 and is the variable and the flux bias offset associated with the variable. If flux_biases evaluates False, then no flux bias is applied or calculated. If None if given, the flux biases are pulled from the database or calculated empirically.
  • flux_bias_num_reads (int, optional, default=1000) – The number of samples to collect per flux bias value.
  • flux_bias_max_age (int, optional, default=3600) – The maximum age (in seconds) allowed for a previously calculated flux bias offset.
Sampler Properties
VirtualGraphComposite.properties dict – Contains one key 'child_properties' which has a copy of the child sampler’s properties.
VirtualGraphComposite.parameters The same parameters as are accepted by the child sampler with an additional parameter ‘apply_flux_bias_offsets’.
Composite Properties
VirtualGraphComposite.children list – A list containig the wrapped sampler.
VirtualGraphComposite.child The first child in children.
Structure Properties
VirtualGraphComposite.nodelist list – The nodes available to the sampler.
VirtualGraphComposite.edgelist list – The edges available to the sampler.
VirtualGraphComposite.adjacency dict[variable, set] – The adjacency structure.
VirtualGraphComposite.structure A namedtuple Structure(nodelist, edgelist, adjacency)
Methods
VirtualGraphComposite.sample(bqm, **parameters) Samples from the given bqm using the instantiated sample method.
VirtualGraphComposite.sample_ising(h, J[, …]) Sample from the given Ising model.
VirtualGraphComposite.sample_qubo(Q, …) Samples from the given QUBO using the instantiated sample method.

Installation

Installation from PyPI:

pip install dwave-system --extra-index-url https://pypi.dwavesys.com/simple

Installation from source:

pip install -r requirements.txt --extra-index-url https://pypi.dwavesys.com/simple
python setup.py

Downloaded with this package is a dependency called dwave-system-tuning that has a restricted license. To view the license details:

from dwave.system.tuning import __license__
print(__license__)

To uninstall the proprietary components:

pip uninstall dwave-system-tuning

License

Apache License

Version 2.0, January 2004

http://www.apache.org/licenses/

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D-Wave

D-Wave Systems is the leader in the development and delivery of quantum computing systems and software, and the world’s only commercial supplier of quantum computers.

Learn more about D-Wave at D-Wave Systems.

Ocean Overview

D-Wave Ocean includes various projects/repositories on GitHub that help solve problems on the D-Wave system.

Learn about D-Wave’s Ocean and how its projects work together at D-Wave Ocean on Read the Docs.

Contributing to Ocean

D-Wave welcomes contributions to Ocean projects.

See how to contribute at Ocean Contributors.

Glossary

The field of quantum computing has many domain-specific terms.

Learn the relevant terminology at Ocean Glossary.