Machine Learning with Quantum Computers

Last updated Aug 10, 2022

• Source zotero
• Type subject
• Status needs revision

Metadata

• CiteKey:: schuldMachineLearningQuantum2021
• Type:: book
• Author:: Maria Schuld, Francesco Petruccione
• Publisher:: Springer
• Year:: 2021
• Format:: PDF

Abstract

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.

Files and Links

• Uri:: http://zotero.org/users/5055703/items/HL3VVILR
• File:: Amazon.com Link; schuld_petruccione_2021_machine_learning_with_quantum_computers.pdf
• Local Library:: Zotero

Tags and Collections

• Keywords:: Machine Learning, Quantum Computing, Quantum Machine Learning, 📙, 📥

Annotations(2/8/2022, 4:38:37 PM)

Chapter 1 Introduction

==“quantum machine learning summarises approaches that use synergies between machine learning and quantum information.”== (p. 1)

==“Quantum machine learning in this narrow sense looks at the opportunities that the current development of quantum computers opens up in the context of intelligent data mining.”== (p. 1)

==“whether quantum computers are better at machine learning depends on how one defines “better”.”== (p. 2)

1.1 Background

1.1.1 Merging Two Disciplines

==“Computers are physical devices based on electronic circuits which process information.”== (p. 2)

==“if microscopic systems such as photons, electrons and atoms are directly used to process information, they require another mathematical description to capture the fact that on small scales, nature behaves radically different from what our intuition teaches us. This mathematical framework is called quantum theory”== (p. 2)

==“A computer whose computations can only be described with the laws of quantum theory is called a quantum computer.”== (p. 3)

==“An entire zoo of quantum algorithms has been proposed and is waiting to be used on physical hardware.”== (p. 3) * https://quantumalgorithmzoo.org/*

==“The most famous language in which quantum algorithms are formulated is the circuit model. The central concept is that of a qubit, which takes the place of a classical bit, as well as quantum gates to perform computations on qubits”== (p. 3)

==“In order to preserve quantum coherence throughout thousands of computational operations, error correction becomes crucial. But error correction for quantum systems turns out to be much more difficult than for classical ones”== (p. 3)

==“A lot of progress has been made in the development of so-called Noisy Intermediate-Scale Quantum (NISQ) devices, which are the first prototypes of what may one day be a full quantum computer.”== (p. 3)

==“Machine learning lies at the intersection of statistics, mathematics and computer science. It analyses how computers can learn from prior examples”== (p. 4)

1.1.2 The Rise of Quantum Machine Learning

==“Proposals that merge the two fields of quantum computing and machine learning have been sporadically put forward since the dawn of quantum computing in the 1980s. Some of the earliest contributions, starting in 1995 [3], looked at quantum models of neural networks.”== (p. 5)

==“In the early 2000s, the question of statistical learning theory in a quantum setting was discussed, but received only limited attention.”== (p. 5)

==“The term “quantum machine learning” came into use only around 2013. Lloyd, Mohseni and Rebentrost [7] mention the expression in their manuscript of 2013.”== (p. 5)

==“In 2014, Peter Wittek, who was instrumental in pushing, leading and critically challenging the field, published a monograph with the title Quantum Machine LearningWhat quantum computing means to data mining”== (p. 5)

==“From 2014 onwards, interest in the topic increased significantly”== (p. 5)

==“Today, quantum machine learning has established itself as an active sub-discipline of quantum computing research, and comes with a range of sub-areas.”== (p. 5)

(p. 6)

1.1.3 Four Intersections

==“a typology introduced by Aimeur, Brassard and Gambs [13]. It distinguishes four approaches of how to combine quantum computing and machine learning”== (p. 6) TODO

==“The CC flavour refers to classical data being processed classically.”== ==“in this context it relates to machine learning based on methods borrowed from quantum information research.”==

==“The QC intersection investigates how machine learning can help with quantum computing.”== (p. 6)

==“This book uses the term “quantum machine learning” as a synonym for the CQ flavour”== ==“Here, the data consist of observations from classical systems, such as text, images or time series of macroeconomic variables, which are fed into a quantum computer for analysis. This requires a quantum-classical interface”==

==“The last flavour, QQ, is closely related to the CQ case and looks at coherent or “quantum data” being processed by a quantum computer.”== (p. 7)

==“most methods for the CQ case port over seamlessly to the QQ case”== (p. 7)

1.1.4 Fault-Tolerant Versus Near-Term Approaches

==“algorithms resulting from the fault-tolerant approach require a full error-corrected quantum computer”== (p. 8)

==“first wave” of quantum machine learning”== (p. 8)

==“second wave” of quantum machine learning”== (p. 8)

1. 30 References/61 Books/2021 Machine Learning with Quantum Computers/Chapters/Chapter 1 - Introduction