Machine Learning with Quantum Computers

Front Cover
Springer Nature, Oct 17, 2021 - Science - 312 pages

This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards.

The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

 

Selected pages

Contents

1 Introduction
1
2 Machine Learning
23
3 Quantum Computing
79
4 Representing Data on a Quantum Computer
147
5 Variational Circuits as Machine Learning Models
177
6 Quantum Models as Kernel Methods
216
7 FaultTolerant Quantum Machine Learning
247
8 Approaches Based on the Ising Model
273
9 Potential Quantum Advantages
288
Index
307
Copyright

Other editions - View all

Machine Learning with Quantum Computers
Maria Schuld,Francesco Petruccione
No preview available - 2021
Machine Learning with Quantum Computers
Maria Schuld,Francesco Petruccione
No preview available - 2022

Common terms and phrases

amplitude encoding amplitude vector ancilla apply arXiv arXiv preprint basis encoding Bayesian binary Boltzmann machines classical machine learning classifier complexity computational basis cost function data points dataset defined density matrix deterministic eigenvalues empirical risk estimation example exponential expression feature map feature space Fourier gates Gaussian generalisation graph Hadamard Hamiltonian Hilbert space Hopfield implement inner product input kernel methods linear models machine learning algorithms machine learning models Markov measurement minimisation neural networks notation number of qubits operator optimal optimisation problem output parameters parametrised perceptron Petruccione Phys probabilistic models probability distribution quantum algorithms quantum annealing quantum circuits quantum computing quantum kernel quantum machine learning quantum system qubits random regularisation representation result RKHS rotation routine runtime Schuld Sect simulation single-qubit speedup step stochastic stochastic gradient descent strategy superposition supervised learning support vector machines training data unitary variational circuit variational quantum

About the author (2021)

Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu. She received her Ph.D. from the University of KwaZulu-Natal in 2017, where she began working on the intersection between quantum computing and machine learning in 2013. Besides her numerous contributions to the field, she is a co-developer for the open-source quantum machine learning software framework PennyLane.

Francesco Petruccione received his Ph.D. (1988) and “Habilitation” (1994) from the University of Freiburg, Germany. Since 2004, he has been a professor of Theoretical Physics at the University of KwaZulu-Natal in Durban, South Africa, where in 2007, he was granted a South African Research Chair for Quantum Information Processing and Communication. He is the co-author of “The Theory of Open Quantum Systems” (Oxford University Press, 2002) and has published more than 250 papers in refereed journals. Francesco Petruccione’s research focuses on open quantum systems and quantum information processing and communication.


Bibliographic information

TitleMachine Learning with Quantum Computers
Quantum Science and Technology
AuthorsMaria Schuld, Francesco Petruccione
Edition2
PublisherSpringer Nature, 2021
ISBN3030830985, 9783030830984
Length312 pages
Subjects ›  › 

Computers / Artificial Intelligence / General
Mathematics / Discrete Mathematics
Mathematics / General
Science / Physics / Quantum Theory
  
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