Supervised Learning with Quantum Computers (Quantum Science and Technology) by Maria Schuld (PDF)

Description

Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine “quantum learning models”. A special focus lies on supervised learning, and applications for near-term quantum devices.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This is a pretty nice text summarizing the state of quantum machine learning. The first two chapters review machine learning and quantum computing, respectively. The following chapters cover encoding schemes for data, inference models, and training models. These last chapters are pretty good for getting a grasp on how quantum machine learning is formulated. My two biggest issues with this text that continue to frustrate me are:1. There are so many notation mistakes in the equations throughout the book. The authors misplace (i.e., use a superscript instead of subscript) or use the incorrect indices notation or change the variable symbol without restating what it represents. I’ve wasted a lot of time figuring things out.2. The examples, although very illustrative, don’t have any code to help grasp what is actually being done. With all the available libraries, this seems like a missed opportunity.I still recommend this just hope the 2nd edition improves.

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