D-Wave Ocean Software Documentation#
dwave-gate is a software package for constructing,
modifying and running quantum circuits on the included simulator.
Quadratic models (BQM, CQM).
dwavebinarycsp: Generates BQMs from
constraint satisfaction problems.
Library to construct a binary quadratic model from a constraint satisfaction problem with small constraints over binary variables.
dwave-cloud-client: API client to
dwave-gate: Package for quantum
A software package for constructing, modifying and running quantum circuits.
dwave-hybrid: Framework for building
A general, minimal Python framework for building hybrid asynchronous decomposition samplers for quadratic unconstrained binary optimization (QUBO) problems.
dwave-inspector: Visualizer for
problems submitted to quantum computers.
A tool for visualizing problems submitted to, and answers received from, a D-Wave structured solver such as an Advantage quantum computer.
dwave-networkx: NetworkX extension.
Extension of NetworkX—a Python language package for exploration and analysis of networks and network algorithms—for users of D-Wave Systems.
Preprocessing tools for quadratic models.
Library containing common preprocessing tools for quadratic models.
algorithms for solving binary 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.
dwave-system: D-Wave samplers
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.