Metadata-Version: 2.1
Name: sqaod
Version: 0.3.1
Summary: A collection of solvers for simulated quantum annealing.
Home-page: https://github.com/shinmorino/sqaod/
Author: Shinya Morino
Author-email: shin.morino@gmail.com
Maintainer: Shinya Morino
Maintainer-email: shin.morino@gmail.com
License: UNKNOWN
Keywords: Simulated quantum annealing,Quantum annealing,Quantum computing,Monte Carlo,OpenMP,GPU,CUDA
Platform: UNKNOWN
Classifier: Operating System :: POSIX :: Linux
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: numpy (>=1.11)

=====
Sqaod
=====

Sqaod is a collection of sovlers for simulated quantum annealing, providing a high-performant and stable implementation to simulate quantum annealing.

This package is intended for researchers and engineers  to explore various problems on qunatum computing with conventional workstations and servers.  Sqaod is also available for deployment in commercial usecases.
Please visit `sqaod website <https://github.com/shinmorino/sqaod>`_ and `sqaod wiki <https://github.com/shinmorino/sqaod/wiki>`_ at github for details.

Installation
------------
In order to use sqaod, you also need to install native libraries.  Please visit `Installation <https://github.com/shinmorino/sqaod/wiki/Installation>`_ for details.


Features
--------

* Solving annealing problems with simple mathmatical definitions. 

  Sqaod is capable to deal with two graphs of **dense graph** and **bipartite graph**.  These graphs have simple mathmatical representations, and directly solved without any modifications.

  * Dense graph is the most generic form of QUBO, and utilized for problems such as TSP.

  * Bipartite graph is for problems that have input and output nodes in graph.  An example is RBM.  

* Two solver algorithm, **brute-force search** and **monte-carlo-based simulated quantum annealer** are implemented.

  * Monte-carlo based simulated quantum annealer is to get approximated solutions for problems with larger number of bits.|br| 
    One can solve problems with thousands of bits for dense graph and bipartite graph with simulated quantum anneaers.

  * Brute-force search is for getting strict solutions for problems with smaller number of bits.
    With brute-force solvers, strict solutions for 30-bit Problem are able to be obtained within tens of seconds when high-end GPUs are utilized.


* Acceerated on CPU and GPU.

  Sqaod solvers have C++- and CUDA-based backends for acceleration.

  * Multi-core CPUs with OpenMP are utilized for CPU-based solvers.
  * NVIDIA GPUs by using CUDA are utilized for GPU-based solvers.

* Able to solve problems with large number of bits.

  Sqaod is a software implementation for simulated quantum annealing.  Solvers are able to deal with problems with a large number of bits, while other hardware devices have limitation on solving large problems.

  Problem sizes are limited by memory amount and/or calculation time.  On recent workstations and servers large amount of DRAM are available, and performance of Sqaod is excellent since it's optimized on modern computing devices.

Release history
---------------

Current version is Beta2(Update1), ver 0.3.1.

* Ver 0.3.1 (Beta2 Update1)

  * No changes in solvers and programming interface
  * Adding environmental checks(library installation, versions).

* Ver 0.3.0 (Beta2)

  * Python interfaces are fixed, and most functionalities are tested.
  * Remaining works are optimizations and documentation, which are going to be made by Beta2 planned in the end of June.


