# D-Wave Ocean Software Documentation#

Ocean software is a suite of tools D-Wave Systems provides on the D-Wave GitHub repository for solving hard problems with quantum computers.

Quantum annealing processors naturally return low-energy solutions; some applications require the real minimum energy (optimization problems) and others require good low-energy samples (probabilistic sampling problems). …

Ocean’s `dwave-gate`

is a software package for constructing,
modifying and running quantum circuits on the included simulator.

Installation is **not needed** if you are using an IDE that
implements “devcontainers”.
…

Run D-Wave’s many open-source examples in GitHub Codespaces with just a mouse click (requires a Leap account).

Leap’s quantum-classical hybrid solvers solve arbitrary application problems formulated as quadratic models. (Try this open-source 3D bin-packing example.)

Ocean also provides a Python framework, dwave-hybrid, for building hybrid asynchronous decomposition samplers.

Sign up for Leap quantum cloud service, which gives you immediate, secure access to D-Wave quantum and hybrid solvers, as well as a wealth of information to help you get started creating quantum applications.

Here you will find an introduction to D-Wave’s quantum computers, their hardware and how they work; the properties and parameters for D-Wave’s solvers; references on formulating problems and best practices in quantum computing; and much more.

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dimod:
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
D-Wave solvers.

##
dwave-gate: Package for quantum
circuits.

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

##
dwave-hybrid: Framework for building
hybrid solvers.

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.

dwave-networkx provides tools for working with Chimera and Pegasus graphs and implementations of graph-theory algorithms on the D-Wave system and other binary quadratic model samplers.

##
dwave-ocean-sdk: Ocean software development kit.

Installer for D-Wave’s Ocean Tools.

##
dwave-preprocessing:
Preprocessing tools for quadratic models.

Library containing common preprocessing tools for quadratic models.

##
dwave-samplers: Classical
algorithms for solving binary quadratic models.

A library that implements the following classical algorithms as samplers for solving binary quadratic models (BQM):

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
and composites.

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.

##
minorminer:
Minor-embeds graphs.

A tool for finding graph minor-embeddings, developed to embed Ising problems onto quantum annealers (QA).

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.