D-Wave Ocean Software Documentation¶
Getting Started shows how to install and begin using Ocean tools.
Concepts defines and describes Ocean concepts and terminology.
The Ocean SDK includes the dwave CLI and the following packages:
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 D-Wave 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 D-Wave structured solver such as a D-Wave 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 D-Wave Systems.
Library containing common preprocessing tools for quadratic models.
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.
An implementation of the MST2 multistart tabu search algorithm for quadratic unconstrained binary optimization (QUBO) problems with a dimod Python wrapper.
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 D-Wave system as the solver.