Welcome to the Quantum Machine Learning repository! This repository is a collection of resources, tutorials, research papers, and code related to the fascinating field of Quantum Machine Learning.
- Prerequisite
- Introduction to Quantum Machine Learning
- Getting Started
- Resources
- Contributing
- License
- Linear algebra
- Complex numbers
- Calculus
- Intermediate Python
- Statistical mechanics
- Quantum physics
- Machine learning
Quantum Machine Learning is an interdisciplinary field that combines principles of quantum physics and machine learning. It explores the potential of quantum computing to solve complex computational problems and enhance machine learning algorithms. This repository aims to provide a curated collection of resources to help you delve into this exciting and rapidly evolving field.
If you're new to Quantum Machine Learning, here are some essential resources to get started:
- Quantum Mechanics: The Theoretical Minimum Book by Leonard Susskind - Quantum Mechanics: The Theoretical Minimum is an excellent book for beginners who want to get familiar with the basic ideas behind quantum mechanics. Art Friedman and Leonard Susskind are both highly-experienced physicists who have studied everything from classical physics to the ideas of the most respected theoretical physicist.
- Quantum Computing for Everyone by Chris Bernhardt - This book is published by MIT Press in 2019. It is an accessible introduction to quantum computing that explains topics such as qubits, entanglement, and quantum teleportation for the general reader.
- Grover’s Algorithm: Quantum Database Search∗ - We review Grover’s algorithm by means of a detailed geometrical interpretation and a worked out example. Some basic concepts of Quantum Mechanics and quantum circuits are also reviewed. This work is intended for nonspecialists which have basic knowledge on undergraduate Linear Algebra.
- An Introduction to Quantum Computing for Non-Physicists - The aim of this paper is to guide computer scientists and other non-physicists through the conceptual and notational barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to harnessing the power of quantum parallelism are explained, including Shor's algorithm, Grover's algorithm, and Hogg's algorithms. We conclude with a discussion of quantum error correction.
- University_of_TorontoX UTQML101x Quantum Machine Learning - The pace of development in quantum computing mirrors the rapid advances made in machine learning and artificial intelligence. It is natural to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-enhanced machine learning. The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging with classical digital computers. We put a strong emphasis on implementing the protocols, using open source frameworks in Python. Prominent researchers in the field will give guest lectures to provide extra depth to each major topic. These guest lecturers include Alán Aspuru-Guzik, Seth Lloyd, Roger Melko, and Maria Schuld. We encourage you to watch (and enjoy) these guest lectures. The guest lectures are, however, optional.
- Qiskit: Quantum machine learning course - This course contains around eight hours of content, and is aimed at self-learners who are comfortable with undergraduate-level mathematics and quantum computing fundamentals. This course will take you through key concepts in quantum machine learning, such as parameterized quantum circuits, training these circuits, and applying them to basic problems. By the end of the course, you'll understand the state of the field, and you'll be familiar with recent developments in both supervised and unsupervised learning such as quantum kernels and general adversarial networks. This course finishes with a project that you can use to showcase what you've learnt.
- Blog/Article Title 1 - Summary or key points.
- Blog/Article Title 2 - Summary or key points.
- Awesome Quantum Machine Learning - A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).
Contributions are welcome! If you have additional resources, tutorials, research papers, or anything related to Quantum Machine Learning that you'd like to add to this repository, please follow the contribution guidelines.
This repository is licensed under the MIT License.