Publisher's Synopsis
Quantum machine learning is a subject in the making, with endless possibilities for applications in the near and long term. Nonetheless, to find out what quantum machine learning has to offer its numerous possible avenues first have to be explored by an interdisciplinary community of scientists and quantum computing enthusiasts. This book in intended to be a starting point for this journey, as it to introduces key concepts, ideas, and algorithms that are the result of the first few years of quantum machine learning research. The aim is to provide a comprehensive literature review and to summarize key topics that appear often in quantum machine learning, to put them into context and make them accessible to a broader audience in order to foster future research and applications.
Key Features:
- An associated Github repository with example code implementations
- A chapter on quantum generative models.
- Accessible reference text useful for both students and researchers.
- A discussion of implementation on different NISQ platforms (squeezed light modes vs trapped ions vs superconducting qubits) and the associated challenges
- Case studies
