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This book highlights novel research work done on cold atom-based quantum networks. Given that one of the main challenges in building the quantum network is the limited entanglement distribution distance, this book presents some state-of-the-art experiments in tackling this challenge and, for the first time, establishes entanglement between quantum memories via metropolitan-scale fiber transmission. This achievement is accomplished by cooperating high-efficiency cold quantum memories, low-loss quantum frequency conversion modules, and long-fiber phase-locking techniques. In the book, the scheme design, experimental setup, data analyses, and numerous technical details are given. Therefore, it suits a broad readership that includes all students, researchers, and technicians who work in quantum information sciences.
Quantum mechanics. Quantumfield theory --- quantumfysica --- Quantum computing. --- Quantum entanglement.
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This textbook introduces quantum computing to readers who do not have much background in linear algebra. The author targets undergraduate and master students, as well as non-CS and non-EE students who are willing to spend about 60 -90 hours seriously learning quantum computing. Readers will be able to write their program to simulate quantum computing algorithms and run on real quantum computers on IBM-Q. Moreover, unlike the books that only give superficial, "hand-waving" explanations, this book uses exact formalism so readers can continue to pursue more advanced topics based on what they learn from this book. Encourages students to embrace uncertainty over the daily classical experience, when encountering quantum phenomena; Uses narrative to start each section with analogies that help students to grasp the critical concept quickly; Uses numerical substitutions, accompanied by Python programming and IBM-Q quantum computer programming, as examples in teaching all critical concepts.
Programming --- Computer architecture. Operating systems --- Computer. Automation --- embedded systems --- quantumcomputers --- computers --- programmeren (informatica) --- Quantum computing.
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This essential creates a lively and vivid understanding of the processes in quantum computers. It explores the quantum phenomena of entanglement and superposition and how they can be used for computing. Coding of information, explanation of simple algorithms, and possible applications are shown. A glossary at the end of the essentials explains the most important terms.
Space research --- Quantum mechanics. Quantumfield theory --- Physics --- quantumfysica --- fysica --- ruimte (astronomie) --- Mathematics --- Quantum computing.
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This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a "classical part" that describes standard machine learning schemes and a "quantum part" that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.
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This unique text/reference explores music with respect to quantum computing, a nascent technology that is advancing rapidly. Quantum computing promises to bring unprecedented higher speed and optimisation for running algorithms. Of course, this will benefit the music industry in one way or another, but also yield new approaches to musical creativity. There is a long history of research into using computers for music since the 1950s, and nowadays, computers are essential for the music economy. Indeed, it is very likely that quantum computers will impact the music industry in times to come. Consequently, a new area of research and development is emerging: quantum computer music. This unprecedented book examines this new field, introducing the fundamentals of quantum computing for musicians and the latest developments by pioneering practitioners. Each chapter focuses on innovative approaches that leverage the quantum-mechanical nature of quantum computing. Any additional theory required for understanding a given approach is supplied in the respective chapter, and plenty of references are provided. The book also includes some tutorials and walk-through examples, in addition to addressing scientific and aesthetic considerations. Written by pioneering experts, the present volume will serve as a first-of-its-kind reference for all those interested in or studying this fascinating and promising new field. Prof. Eduardo Reck Miranda is a composer and a professor in Computer Music at Plymouth University, UK, where he is a director of the Interdisciplinary Centre for Computer Music Research (ICCMR). His previous publications include the Springer titles Handbook of Artificial Intelligence for Music, Guide to Unconventional Computing for Music, Guide to Brain-Computer Music Interfacing and Guide to Computing for Expressive Music Performance.
Human sciences --- Programming --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- quantumcomputers --- applicatiebeheer --- sociale media --- apps --- architectuur (informatica) --- Quantum computing. --- Computer music --- Analysis, appreciation.
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This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today's NISQ hardware, the algorithm is evaluated on IBM's quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning. About the author Leonhard Kunczik obtained his Dr. rer. nat. in 2021 in Quantum Reinforcement Learning from the Universität der Bundeswehr München as a member of the COMTESSA research group. Now, he continues his research as a project leader at the forefront of Quantum Machine Learning and Optimization in the context of Operations Research and Cyber Security.
Criminal law. Criminal procedure --- Programming --- Computer architecture. Operating systems --- Computer. Automation --- veiligheidsdiensten --- programmeren (informatica) --- computerbeveiliging --- computercriminaliteit --- Quantum computing. --- Aprenentatge per reforç (Intel·ligència artificial)
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This book highlights the novel research in quantum memory networking, especially quantum memories based on cold atomic ensembles. After discussing the frontiers of quantum networking research and building a DLCZ-type quantum memory with cold atomic ensemble, the author develops the ring cavity enhanced quantum memory and demonstrates a filter-free quantum memory, which significantly improves the photon-atom entanglement. The author then realizes for the first time the GHZ-type entanglement of three separate quantum memories, a building block of 2D quantum repeaters and quantum networks. The author also combines quantum memories and time-resolved measurements, and reports the first multiple interference of three single photons with different colors. The book is of good reference value for graduate students, researchers, and technical personnel in quantum information sciences.
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