Supervised Learning with Quantum Computers

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. Front Matter ....Pages i-xiii Introduction (Maria Schuld, Francesco Petruccione)....Pages 1-19 Machine Learning (Maria Schuld, Francesco Petruccione)....Pages 21-73 Quantum Information (Maria Schuld, Francesco Petruccione)....Pages 75-125 Quantum Advantages (Maria Schuld, Francesco Petruccione)....Pages 127-137 Information Encoding (Maria Schuld, Francesco Petruccione)....Pages 139-171 Quantum Computing for Inference (Maria Schuld, Francesco Petruccione)....Pages 173-210 Quantum Computing for Training (Maria Schuld, Francesco Petruccione)....Pages 211-245 Learning with Quantum Models (Maria Schuld, Francesco Petruccione)....Pages 247-272 Prospects for Near-Term Quantum Machine Learning (Maria Schuld, Francesco Petruccione)....Pages 273-279 Back Matter ....Pages 281-287