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.