Quantum Machine Learning Tutorial

A Hands-on Tutorial for Machine Learning Practitioners and Researchers

Resources

Welcome to the Resources page, where you can find valuable materials to aid your journey in Quantum Machine Learning. Below is a curated list of textbooks, software and hardware platforms, lecture notes, and other open-source libraries.

Textbooks

  1. Quantum Computation and Quantum Information
    Author: Michael A. Nielsen and Isaac L. Chuang
    This is one of the most widely used textbooks in the field of quantum computing. It covers the fundamental concepts of quantum mechanics, quantum computation, and quantum algorithms in depth. A must-read for anyone getting started with quantum computing.

  2. The Theory of Quantum Information
    Author: John Watrous
    This is a book on the mathematical theory of quantum information, focusing on a formal presentation of definitions, theorems, and proofs. It is primarily intended for graduate students and researchers having some familiarity with quantum information and computation.

  3. Dive into Deep Learning
    Author: Aston Zhang, Zack C. Lipton, Mu Li and Alex J. Smola
    This book covers foundational deep learning techniques and practical applications with math, discussions and code written in Python.

Open-source Libraries

  1. PennyLane
    PennyLane is a powerful open-source software library for quantum machine learning, quantum computing, and quantum chemistry. It integrates well with popular ML frameworks like PyTorch and TensorFlow, allowing users to combine quantum circuits with classical neural networks.

  2. Qiskit
    Qiskit is an open-source quantum computing framework from IBM. It is one of the most popular tools for building and simulating quantum circuits, and also includes a library for quantum machine learning. Qiskit’s machine learning module allows users to implement quantum-enhanced algorithms easily.

  3. TensorFlow Quantum
    TensorFlow Quantum is an open-source library for quantum machine learning, built on top of TensorFlow. It offers tools for training quantum models and combining classical and quantum data to build hybrid quantum-classical models.

  4. Cirq
    Developed by Google, Cirq is an open-source Python library for designing, simulating, and executing quantum circuits. While primarily targeted towards quantum computing research, Cirq can also be utilized for quantum machine learning tasks when paired with machine learning frameworks.

Hardware Platforms

  1. IBM Quantum Experience
    IBM offers an online quantum computing platform that allows users to develop, run, and test quantum algorithms on actual quantum processors via the cloud. It’s a great way to access quantum hardware for research and experiment with quantum machine learning algorithms.

  2. Quafu Cloud Quantum Computing Platform
    Quafu is an online quantum computing platform that offers users free access to a range of physical quantum devices, supported by the Beijing Academy of Quantum Information Sciences (BAQIS).

  3. Amazon Braket
    Amazon Braket is a fully managed quantum computing service that helps you get started with quantum computing. It supports various quantum computing frameworks and allows you to experiment with quantum machine learning algorithms.

Lecture Notes

  1. Quantum Computation Lecture Notes by Professor John Preskill

  2. Advanced Topics in Quantum Information Theory by Professor John Watrous

By providing these resources, we hope to give you the tools and references needed to explore the exciting field of Quantum Machine Learning. Whether you’re just starting or advancing your research, these materials will be invaluable in your learning journey.