Quantum Annealing Python Packages

openjij

OpenJij : Framework for the Ising model and QUBO.

jij-cimod

C++ library for a binary quadratic model

qibo

A full-stack framework for quantum computing.

qibojit

Accelerating Qibo simulation with just-in-time compilation.

accelbrainbeat

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

qqa

Quasi-Quantum Annealing (QQA): a GPU solver for combinatorial & spin-glass optimisation — PyTorch library + live Streamlit dashboard. ICLR 2025.

qibotf

Simulation tools based on tensorflow.

pysummarization

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

qlasskit

A python-to-quantum compiler

pyqlearning

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

pydbm

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

accel-brain-base

accel-brain-base is a basic library of the Deep Learning for rapid development at low cost. This library makes it possible to design and implement deep learning, which must be configured as a complex system, by combining a plurality of functionally differentiated modules such as a Deep Boltzmann Machines(DBMs), an Auto-Encoder, an Encoder/Decoder based on Long Short-Term Memory(LSTM), a Convolutional Auto-Encoder(CAE), and Transformer.

engioptiqa

Engineering Optimization with Quantum Annealing

pygan

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

qunfold

Quantum Annealing for distribution unfolding in experimental High-Energy Physics

sqaod

A collection of solvers for simulated quantum annealing.

pycomposer

pycomposer is Python library for Algorithmic Composition or Automatic Composition based on the stochastic music theory and the Statistical machine learning problems. Especialy, this library provides apprication of the Algorithmic Composer based on Generative Adversarial Networks(GANs) and Conditional Generative Adversarial Networks(Conditional GANs).

sawatabi

Sawatabi is an application framework to develop and run stream-data-oriented Ising applications with quantum annealing.

gsgmorph

The Graph/Subgraph Isomorphism Library for Quantum Annealers

qubo-nn

Classifying, auto-encoding and reverse-engineering QUBO matrices

tno-quantum-problems-n-minus-1

N-minus-1 Quantum Solver

quron

Auto-construct QUBO via AutoEncoder + Factorization Machine.

algo-wars

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.