This monograph is motivated by a number of recent developments that appear to define a possible new role for researchers with an engineering profile. First, there are now several software libraries - such as IBM's Qiskit, Google's Cirq, and Xanadu's PennyLane - that make programming quantum algorithms more accessible, while also providing cloud-based access to actual quantum computers. Second, a new framework is emerging for programming quantum algorithms to be run on current quantum hardware: quantum machine learning. In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook An Introduction to Quantum Machine Learning for Engineers for free.
- Title
- An Introduction to Quantum Machine Learning for Engineers
- Publisher
- Now Publishers
- Author(s)
- Osvaldo Simeone
- Published
- 2022-07-30
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 238
- Language
- English
- ISBN-10
- 1638280584
- ISBN-13
- 9781638280583
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
1 Classical Bit (Cbit) and Quantum Bit (Qubit) 1.1 Introduction 1.2 Random Classical Bit 1.3 Qubit 1.4 Single-Qubit Quantum Gates 1.5 Amplitude Diagrams 1.6 Interference 1.7 Conclusions 1.8 Recommended Resources 1.9 Problems 2 Classical Bits (Cbits) and Quantum Bits (Qubits) 2.1 Introduction 2.2 Multiple Random Classical Bits 2.3 Multiple Qubits 2.4 Quantum Circuits and Local Operations 2.5 Entanglement 2.6 Multi-Qubit Quantum Gates 2.7 Creating Entanglement 2.8 Amplitude Diagrams 2.9 Superdense Coding 2.10 Trading Quantum and Classical Resources 2.11 Conclusions 2.12 Recommended Resources 2.13 Problems 3 Generalizing Quantum Measurements (Part I) 3.1 Introduction 3.2 Measurements in an Arbitrary Orthonormal Basis 3.3 Partial Measurements 3.4 Non-Selective Partial Measurements and Decoherence 3.5 Density Matrices 3.6 Partial Trace 3.7 Conclusions 3.8 Recommended Resources 3.9 Problems 4 Quantum Computing 4.1 Introduction 4.2 Gate-Based Model of Quantum Computation 4.3 Computing Binary Functions and Quantum RAM 4.4 Deutsch's Problem and Quantum Parallelism 4.5 Phase Kick-Back 4.6 Validity of Deutsch's Algorithm 4.7 No Cloning Theorem 4.8 Classical Cloning: Basis-Copying Gate 4.9 Conclusions 4.10 Recommended Resources 4.11 Problems 5 Generalizing Quantum Measurements (Part II) 5.1 Introduction 5.2 Projective Measurements 5.3 Observables 5.4 Implementing Projective Measurements 5.5 Quantum Error Correction 5.6 Implementing Projective Measurements with Ancillas 5.7 Positive Operator-Valued Measurements 5.8 Quantum Channels 5.9 Conclusions 5.10 Recommended Resources 5.11 Problems 6 Quantum Machine Learning 6.1 Introduction 6.2 What is Quantum Machine Learning? 6.3 A Taxonomy of Quantum Machine Learning 6.4 Ansatz and Parametrized Quantum Circuits 6.5 Cost Functions for Quantum Machine Learning 6.6 Variational Quantum Eigensolver 6.7 Unsupervised Learning for Generative Models 6.8 Supervised Learning 6.9 Beyond Generic Ansatzes 6.10 Beyond Angle Encoding 6.11 Conclusions 6.12 Recommended Resources 6.13 Problems Acknowledgements References
