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