D-Wave Ocean tools are documented on Read the Docs. Click on a link below for the documentation for each tool (or the link in parentheses for the tool repository located at D-Wave on GitHub).
|:std:doc:`dimod <dimod:index>` (repo)||
Shared API for binary quadratic :term:`sampler`s.
dimod provides a binary quadratic model (BQM) class that contains :term:`Ising` and quadratic unconstrained binary optimization (:term:`QUBO`) models used by samplers such as the D-Wave system. It also provides utilities for constructing new samplers and composed samplers.
|:std:doc:`dwavebinarycsp <binarycsp:index>` (repo)||Library to construct a binary quadratic model from a constraint satisfaction problem with small constraints over binary variables.|
|:std:doc:`dwave-cloud-client <cloud-client:index>` (repo)||Minimal implementation of the REST interface used to communicate with D-Wave :term:`Sampler` API (SAPI) servers.|
|:std:doc:`dwave_neal <neal:index>` (repo)||An implementation of a simulated annealing sampler.|
|:std:doc:`dwave_networkx <networkx:index>` (repo)||
Extension of NetworkX—a Python language package for exploration and analysis of networks and network algorithms—for users of D-Wave Systems.
|dwave-ocean-sdk (repo)||Installer for D-Wave's Ocean Tools.|
|:std:doc:`dwave-system <system:index>` (repo)||
Basic API for easily incorporating the D-Wave system as a :term:`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 :term:`minor-embedding`, optimize chain strength, etc.
|:std:doc:`penaltymodel <penaltymodel:index>` (repo)||
Includes a local cache for penalty models and a factory that generates penalty models using SMT solvers.
|:std:doc:`minorminer <minorminer:index>` (repo)||
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.
|:std:doc:`qbsolv <qbsolv:index>` (repo)||A decomposing solver that finds a minimum value of a large quadratic unconstrained binary optimization (:term:`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.|