Given the extensive application of random walks in virtually every science related discipline, we may be at the threshold of yet another problem solving paradigm with the advent of quantum walks. Over the past decade, quantum walks have been explored for their non-intuitive dynamics, which may hold the key to radically new quantum algorithms. This growing interest has been paralleled by a flurry of research into how one can implement quantum walks in laboratories. This book presents numerous proposals as well as actual experiments for such a physical realization, underpinned by a wide range of quantum, classical and hybrid technologies.
Search algorithms aim to find solutions or objects with specified properties and constraints in a large solution search space or among a collection of objects. A solution can be a set of value assignments to variables that will satisfy the constraints or a sub-structure of a given discrete structure. In addition, there are search algorithms, mostly probabilistic, that are designed for the prospective quantum computer. This book demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. Although it is targeted to a wide group of readers: researchers, graduate students, and practitioners, it does not offer an exhaustive coverage of search algorithms and applications. The chapters are organized into three parts: Population-based and quantum search algorithms, Search algorithms for image and video processing, and Search algorithms for engineering applications.
Practical quantum computing still seems more than a decade away, and researchers have not even identified what the best physical implementation of a quantum bit will be. There is a real need in the scientific literature for a dialogue on the topic of lessons learned and looming roadblocks. This reprint from Quantum Information Processing is dedicated to the experimental aspects of quantum computing and includes articles that 1) highlight the lessons learned over the last 10 years, and 2) outline the challenges over the next 10 years. The special issue includes a series of invited articles that discuss the most promising physical implementations of quantum computing. The invited articles were to draw grand conclusions about the past and speculate about the future, not just report results from the present.
This book discusses the elementary ideas and tools needed for open quantum systems in a comprehensive manner. The emphasis is given to both the traditional master equation as well as the functional (path) integral approaches. It discusses the basic paradigm of open systems, the harmonic oscillator and the two-level system in detail. The traditional topics of dissipation and tunneling, as well as the modern field of quantum information, find a prominent place in the book. Assuming a basic background of quantum and statistical mechanics, this book will help readers familiarize with the basic tools of open quantum systems. Open quantum systems is the study of quantum dynamics of the system of interest, taking into account the effects of the ambient environment. It is ubiquitous in the sense that any system could be envisaged to be surrounded by its environment which could naturally exert its influence on it. Open quantum systems allows for a systematic understanding of irreversible processes such as decoherence and dissipation, of the essence in order to have a correct understanding of realistic quantum dynamics and also for possible implementations. This would be essential for a possible development of quantum technologies.
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 topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
This book offers the revised and completed notes of lectures given at the 2007 conference, "Quantum Potential Theory: Structures and Applications to Physics." These lectures provide an introduction to the theory and discuss various applications.
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.
Covering both theory and progressive experiments, Quantum Computing: From Linear Algebra to Physical Realizations explains how and why superposition and entanglement provide the enormous computational power in quantum computing. This self-contained, classroom-tested book is divided into two sections, with the first devoted to the theoretical aspect
Off-shell science deals with the quantum field in which the dispersion relation between energy and momentum is invalid. A typical example of such the quantum field is the dressed photon (DP) that creates by the interaction among photons, electrons, and phonons in a nano-particle. This field is complementary to the on-shell quantum field (photons in a macroscopic space). Off-Shell Applications in Nanophotonics: Dressed Photon Science and Technology reviews the experimental/theoretical studies and shows the route that should be taken to establish off-shell science in the future. A variety of phenomena originate from the DP, and phenomena analogous to them have been found among physical, chemical, and biological phenomena. This indicates that off-shell quantum fields are universal and essential constituent elements of nature. By noting this, readers will be able to use off-shell science to develop new technologies. This book presents i) the reasons why the off-shell scientific theory is required, ii) the nature of the dressed photon by presenting experimental results, iii) tentative theoretical description of the dressed photon, iv) disruptive innovations (nano-optical devices, nano-fabrication technology, energy conversion technology, and silicon light-emitting diodes/lasers), and v) genuine theoretical approaches (based on spatio-temporal vortex hydrodynamics, quantum probability, quantum measurement, and micro-macro duality). It will appeal to materials scientists, engineers and physicists working in the areas of optics and photonics. - Explains the fundamental concepts behind off-shell science, and how it differs from traditional nanophotonics - Presents a range of simulation models demonstrating major off-shell models - Assesses the major challenges for researchers wanting to employ off-shell-based experimental techniques
