DWave Ocean Software Documentation¶
Ocean software is a suite of tools DWave Systems provides on the DWave GitHub repository for solving hard problems with quantum computers.
Getting Started shows how to install and begin using Ocean tools.
Concepts defines and describes Ocean concepts and terminology.
Packages¶
The SDK includes the dwave CLI and the following packages:
Shared API for binary quadratic samplers. Provides a binary quadratic model (BQM) class that contains Ising and quadratic unconstrained binary optimization (QUBO) models used by samplers such as the DWave system. Also provides utilities for constructing new samplers and composed samplers. 

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

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

An implementation of a steepest descent solver for binary quadratic models. 

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

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

An implementation of a simulated annealing sampler. 

Extension of NetworkX—a Python language package for exploration and analysis of networks and network algorithms—for users of DWave Systems. dwavenetworkx provides tools for working with Chimera graphs and implementations of graphtheory algorithms on the DWave system and other binary quadratic model samplers. 

Installer for DWave’s Ocean Tools. 

Basic API for easily incorporating the DWave system as a sampler in the DWave 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 postprocessing—that can be used with DWaveSampler to handle minorembedding, optimize chain strength, etc. 

An implementation of the MST2 multistart tabu search algorithm for quadratic unconstrained binary optimization (QUBO) problems with a dimod Python wrapper. 

An approach to solve a constraint satisfaction problem (CSP) using an Ising model or a QUBO, is to map each individual constraint in the CSP to a ‘small’ Ising model or QUBO. Includes a local cache for penalty models and a factory that generates penalty models using SMT solvers. 

A tool for finding graph minorembeddings, 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. 

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

A decomposing solver that finds a minimum value of a large quadratic unconstrained binary optimization (QUBO) problem by splitting it into pieces. The pieces are solved using a classical solver running the tabu algorithm. qbsolv also enables configuring a DWave system as the solver. 