# Packages¶

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**dwavebinarycsp** — Generates BQMs from constraint satisfaction problems. docs code

Library to construct a binary quadratic model from a constraint satisfaction problem with small constraints over binary variables.

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**dwave-cloud-client** — API client to D-Wave solvers. docs code

Minimal implementation of the REST interface used to communicate with D-Wave Sampler API (SAPI) servers.

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**dwave-gate** — Package for quantum circuits. docs code

A software package for constructing, modifying and running quantum circuits.

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**dwave-hybrid** — Framework for building hybrid solvers. docs code

A general, minimal Python framework for building hybrid asynchronous decomposition samplers for quadratic unconstrained binary optimization (QUBO) problems.

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**dwave-inspector** — Visualizer for problems submitted to quantum computers. docs code

A tool for visualizing problems submitted to, and answers received from, a D-Wave structured solver such as a D-Wave 2000Q quantum computer.

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**dwave-networkx** — NetworkX extension. docs code

Extension of NetworkX—a Python language package for exploration and analysis of networks and network algorithms—for users of D-Wave Systems.

dwave-networkx provides tools for working with Chimera and Pegasus graphs and implementations of graph-theory algorithms on the D-Wave system and other binary quadratic model samplers.

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**dwave-preprocessing** — Preprocessing tools for quadratic models. docs code

Library containing common preprocessing tools for quadratic models.

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**dwave-samplers** — Classical algorithms for solving binary quadratic models. docs code

A library that implements the following classical algorithms as samplers for solving binary quadratic models (BQM):

Planar: an exact solver for planar Ising problems with no linear biases.

Random: a sampler that draws uniform random samples.

Simulated Annealing: a probabilistic heuristic for optimization and approximate Boltzmann sampling well suited to finding good solutions of large problems.

Steepest Descent: a discrete analogue of gradient descent, often used in machine learning, that quickly finds a local minimum.

Tabu: a heuristic that employs local search with methods to escape local minima.

Tree Decomposition: an exact solver for problems with low treewidth.

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**dwave-system** — D-Wave samplers and composites. docs code

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.

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**minorminer** — Minor-embeds graphs. docs code

A tool for finding graph minor-embeddings, developed to embed Ising problems onto quantum annealers (QA).

While it can be used to find minors in arbitrary graphs, it is particularly geared towards the state of the art in QA: problem graphs of a few to a few hundred variables, and hardware graphs of a few thousand qubits.

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**pyqubo** — Creates quadratic models from mathematical expressions. docs code

A package that helps you create QUBOs and Ising models from flexible mathematical expressions.