# Quantum Machine Learning

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- Author : Peter Wittek
- Publisher : Academic Press
- Release : 10 September 2014
- ISBN : 9780128010990
- Page : 176 pages
- Rating : 4.5/5 from 103 voters

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Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

### Quantum Machine Learning

- Author : Peter Wittek
- Publisher : Academic Press
- Release Date : 2014-09-10
- ISBN : 9780128010990

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary

### Machine Learning with Quantum Computers

- Author : Maria Schuld,Francesco Petruccione
- Publisher : Springer Nature
- Release Date : 2021-11-18
- ISBN : 9783030830984

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

### Quantum Machine Learning: An Applied Approach

- Author : Santanu Ganguly
- Publisher : Apress
- Release Date : 2021-08-11
- ISBN : 1484270975

Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of

### Supervised Learning with Quantum Computers

- Author : Maria Schuld,Francesco Petruccione
- Publisher : Springer
- Release Date : 2018-08-30
- ISBN : 9783319964249

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

### Quantum Machine Learning

- Author : Siddhartha Bhattacharyya,Indrajit Pan,Ashish Mani,Sourav De,Elizabeth Behrman,Susanta Chakraborti
- Publisher : Walter de Gruyter GmbH & Co KG
- Release Date : 2020-06-08
- ISBN : 9783110670721

Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely

### Quantum Machine Learning With Python

- Author : Santanu Pattanayak
- Publisher : Apress
- Release Date : 2021-03-29
- ISBN : 1484265211

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others. You'll start by reviewing the fundamental concepts of Quantum Computing,

### Hands-On Quantum Machine Learning With Python

- Author : Frank Zickert
- Publisher : Independently Published
- Release Date : 2021-06-19
- ISBN : 9798516564499

You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But

### Machine Learning Meets Quantum Physics

- Author : Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller
- Publisher : Springer Nature
- Release Date : 2020-06-03
- ISBN : 9783030402457

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations

### Programming Quantum Computers

- Author : Eric R. Johnston,Nic Harrigan,Mercedes Gimeno-Segovia
- Publisher : O'Reilly Media
- Release Date : 2019-07-03
- ISBN : 9781492039655

Quantum computers are set to kick-start a second computing revolution in an exciting and intriguing way. Learning to program a Quantum Processing Unit (QPU) is not only fun and exciting, but it's a way to get your foot in the door. Like learning any kind of programming, the best way to proceed is by getting your hands dirty and diving into code. This practical book uses publicly available quantum computing engines, clever notation, and a programmer’s mindset to get

### Limitations and Future Applications of Quantum Cryptography

- Author : Kumar, Neeraj,Agrawal, Alka,Chaurasia, Brijesh K.,Khan, Raees Ahmad
- Publisher : IGI Global
- Release Date : 2020-12-18
- ISBN : 9781799866794

The concept of quantum computing is based on two fundamental principles of quantum mechanics: superposition and entanglement. Instead of using bits, qubits are used in quantum computing, which is a key indicator in the high level of safety and security this type of cryptography ensures. If interfered with or eavesdropped in, qubits will delete or refuse to send, which keeps the information safe. This is vital in the current era where sensitive and important personal information can be digitally shared

### Principles of Quantum Artificial Intelligence

- Author : Andreas Wichert
- Publisher : World Scientific
- Release Date : 2013-10-23
- ISBN : 9789814566766

In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems. Contents:IntroductionComputationProblem SolvingInformationReversible AlgorithmsProbabilityIntroduction to Quantum PhysicsComputation with QubitsPeriodicitySearchQuantum Problem-SolvingQuantum CognitionRelated Approaches Readership:

### Research Anthology on Advancements in Quantum Technology

- Author : Management Association, Information Resources
- Publisher : IGI Global
- Release Date : 2021-03-19
- ISBN : 9781799887409

Quantum technology has arrived as one of the most important new topics of research, as it is the newest way to create computing power, harness secure communications, and use sensitive measurement methods that surpass the capabilities of modern supercomputers. If successfully developed, quantum computers and technology will be able to perform algorithms at impressively quick rates and solve problems that were previously deemed impossible. This technology will disrupt what is already known about computing and will be able to reach

### Data Management, Analytics and Innovation

- Author : Neha Sharma,Amlan Chakrabarti,Valentina Emilia Balas,Jan Martinovic
- Publisher : Springer Nature
- Release Date : 2020-09-18
- ISBN : 9789811556197

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the

### Quantum Processes Systems, and Information

- Author : Benjamin Schumacher,Michael Westmoreland
- Publisher : Cambridge University Press
- Release Date : 2010-03-25
- ISBN : 9781139487207

A new and exciting approach to the basics of quantum theory, this undergraduate textbook contains extensive discussions of conceptual puzzles and over 800 exercises and problems. Beginning with three elementary 'qubit' systems, the book develops the formalism of quantum theory, addresses questions of measurement and distinguishability, and explores the dynamics of quantum systems. In addition to the standard topics covered in other textbooks, it also covers communication and measurement, quantum entanglement, entropy and thermodynamics, and quantum information processing. This textbook gives

### Future Directions for Intelligent Systems and Information Sciences

- Author : Nikola Kasabov
- Publisher : Physica
- Release Date : 2013-11-11
- ISBN : 9783790818567

This edited volume comprises invited chapters that cover five areas of the current and the future development of intelligent systems and information sciences. Half of the chapters were presented as invited talks at the Workshop "Future Directions for Intelligent Systems and Information Sciences" held in Dunedin, New Zealand, 22-23 November 1999 after the International Conference on Neuro-Information Processing (lCONIPI ANZIISI ANNES '99) held in Perth, Australia. In order to make this volume useful for researchers and academics in the broad area