========
Packages
========
.. packages-start-marker
.. dropdown:: `dimod `_:
Quadratic models (BQM, CQM).
Shared API for binary quadratic :term:`sampler`\ s. 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. Also provides utilities
for constructing new samplers and composed samplers.
:bdg-link-primary:`code `
.. dropdown:: :ref:`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.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-cloud-client `: API client to
D-Wave solvers.
Minimal implementation of the REST interface used to communicate
with D-Wave :term:`Sampler` API (SAPI) servers.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-gate `: Package for quantum
circuits.
A software package for constructing, modifying and running quantum
circuits.
:bdg-link-primary:`code `
.. dropdown:: :ref:`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.
:bdg-link-primary:`code `
.. dropdown:: :ref:`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.
:bdg-link-primary:`code `
.. dropdown:: :ref:`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 :term:`Chimera` and
:term:`Pegasus` graphs and implementations of graph-theory algorithms
on the D-Wave system and other binary quadratic model :term:`sampler`\ s.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-ocean-sdk `: Ocean software development kit.
Installer for D-Wave's Ocean Tools.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-preprocessing `:
Preprocessing tools for quadratic models.
Library containing common preprocessing tools for quadratic models.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-samplers `: Classical
algorithms for solving binary quadratic models.
A library that implements the following classical algorithms as
:term:`samplers` for solving
:term:`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.
:bdg-link-primary:`code `
.. dropdown:: :ref:`dwave-system `: D-Wave samplers
and composites.
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.
:bdg-link-primary:`code `
.. dropdown:: `minorminer `_:
Minor-embeds graphs.
A tool for finding graph :term:`minor-embedding`\ s, developed to
embed :term:`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.
:bdg-link-primary:`code `
.. dropdown:: :ref:`penaltymodel `: Maps constraints
to binary quadratic models.
An approach to solve a constraint satisfaction problem (CSP) using
an :term:`Ising` model or a :term:`QUBO`, is to map each individual
constraint in the CSP to a ‘small’ Ising model or QUBO.
Includes a local cache for penalty models and a factory that
generates penalty models using SMT solvers.
:bdg-link-primary:`code `
.. dropdown:: `pyqubo `_:
Creates quadratic models from mathematical expressions.
A package that helps you create QUBOs and Ising models from
flexible mathematical expressions.
:bdg-link-primary:`code `
.. packages-end-marker
.. toctree::
:hidden:
:maxdepth: 1
docs_dimod/sdk_index
docs_binarycsp/sdk_index
docs_cloud/sdk_index
docs_gate/sdk_index
docs_greedy/sdk_index
docs_hybrid/sdk_index
docs_inspector/sdk_index
docs_neal/sdk_index
docs_dnx/sdk_index
docs_preprocessing/sdk_index
docs_samplers/index
docs_system/sdk_index
docs_tabu/sdk_index
docs_minorminer/source/sdk_index
docs_penalty/sdk_index
docs_pyqubo
docs_qbsolv