New quantum software

New Pytorch-Based Platform is coming!": Our team is excited to announce the upcoming release of a new quantum machine learning platform, based on PyTorch. This platform is designed to offer enhanced capabilities and efficiency in quantum computing research and application. Stay tuned for its launch!

Development history

Previous Software Development: Xin Wang’s group at Baidu Research made contributions to the field of quantum software development. Specifically, they were involved in the development of Paddle Quantum, a quantum machine learning toolkit. This toolkit is part of the Baidu Quantum Platform, which is built on Baidu’s deep learning platform, PaddlePaddle. Paddle Quantum aims to integrate quantum computing with artificial intelligence, a fusion that promises to overcome bottlenecks in quantum research and development using current AI technologies.

Paddle Quantum: Paddle Quantum is a quantum machine learning toolkit developed on Baidu’s deep learning platform, PaddlePaddle. It’s the first deep learning platform in China to support quantum machine learning, allowing for the building and training of quantum neural network models.

The latest 2.4.0 version of Paddle Quantum offers an extensive list of over 50 tutorials in both English and Chinese. It is highly efficient in constructing quantum neural networks and incorporates numerous optimization tools, including a GPU mode for enhanced performance. Additionally, this version includes specialized toolboxes designed for chemistry and optimization, distributed quantum information processing, and a range of self-developed quantum machine learning algorithms, demonstrating its broad applicability and advanced capabilities in the field of quantum computing.

The comprehensive API documents is here.

More details can be found on the official website, Github and PyPI.

QAPP: QAPP, a suite of quantum computing solution tools built upon the QComputeSDK, delivers quantum computing services for diverse problem domains such as quantum chemistry, combinatorial optimization, and machine learning. It offers users a comprehensive quantum computing application development platform, addressing their specific needs in artificial intelligence, financial technology, education, and research.

The QAPP architecture adheres to a holistic development flow from application conception to real machine execution, comprising four key modules: Application, Algorithm, Circuit, and Optimizer. This four modules incorporate the entire process of quantum computing application development, from problem formulation to solution optimization, which provides users with a unified and convenient interface for quantum computing application development.

The comprehensive API documents is here.

More details can be found on the official website and Github.

Software recommendation

There are plenty of useful quantum computing toolboxes that have been developed and available to the public. Those softwares can be used in different research aspects including, quantum information theory, quantum machine learning, quantum algorithm design, and etc. Here we list some most commonly used quantum computing software and packages organized by different platforms:

Python:

  1. Qiskit:

    • Developed by IBM Quantum.
    • Comprehensive open-source quantum computing software development framework.
    • Provides tools for circuit design, simulation, and execution on IBM Quantum hardware.
    • Qiskit Official Website
  2. Cirq:

    • Developed by Google.
    • Open-source framework for designing, simulating, and running quantum circuits on Google’s quantum processors.
    • Cirq Official Website
  3. Forest SDK (Rigetti):

    • Developed by Rigetti Computing.
    • Includes tools like pyQuil for programming quantum computers and Forest for quantum simulation.
    • Forest SDK Official Website

Matlab:

  1. Quantum Development Kit for MATLAB (QDK-M):

  2. QETLAB:

Cross-Platform:

  1. ProjectQ:

  2. QuTiP (Quantum Toolbox in Python):

    • Open-source software for simulating the dynamics of open quantum systems.
    • Provides tools for quantum information processing and quantum optics.
    • QuTiP Official Website
  3. OpenFermion:

  4. PennyLane:

    • Quantum machine learning library.
    • Allows the integration of various quantum devices and simulators into machine learning workflows.
    • PennyLane Official Website

These are just a few examples of popular quantum computing software and packages. Depending on your specific research needs, you may find different tools more suitable. Always check the official documentation for the most up-to-date information on installation and usage.

Contribution list

Acknowledgement to those who have contributed to this project:

NameQAPPPQ
Zhixin Song
Zelin Meng 
Yifang Chen
Youle Wang
Guangxi Li
Yixuan Song
Zixian Yan
Zihe Wang
Chenfeng Cao
Hongbin Ren
Jiaqing Jiang
Ranyiliu Chen
Benchi Zhao
Xuanqiang Zhao
Qinghe Wang
Sizhuo Yu 
Yingjian Liu 
Maoran Li
Kaiyan Shi
Zihan Xia
Hanzhe Xi
Jiahui Wang
Xia Liu
Hongshun Yao
Jiaxin Huang
Haokai Zhang
Mujin Li
Geng Liu
Chengkai Zhu
Yin Mo
Ruilin Ye
Zhan Yu
Lei Zhang
Chenghong Zhu
Mingrui Jing
Zhenduo Wang
Peize Ding
Luozhen Li
Yifei Chen
Zhiping Liu