Neural-Network Simulation of Strongly Correlated Quantum Systems

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Publisher : Springer Nature
ISBN 13 : 3030527158
Total Pages : 205 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Neural-Network Simulation of Strongly Correlated Quantum Systems by : Stefanie Czischek

Download or read book Neural-Network Simulation of Strongly Correlated Quantum Systems written by Stefanie Czischek and published by Springer Nature. This book was released on 2020-08-27 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantum systems with many degrees of freedom are inherently difficult to describe and simulate quantitatively. The space of possible states is, in general, exponentially large in the number of degrees of freedom such as the number of particles it contains. Standard digital high-performance computing is generally too weak to capture all the necessary details, such that alternative quantum simulation devices have been proposed as a solution. Artificial neural networks, with their high non-local connectivity between the neuron degrees of freedom, may soon gain importance in simulating static and dynamical behavior of quantum systems. Particularly promising candidates are neuromorphic realizations based on analog electronic circuits which are being developed to capture, e.g., the functioning of biologically relevant networks. In turn, such neuromorphic systems may be used to measure and control real quantum many-body systems online. This thesis lays an important foundation for the realization of quantum simulations by means of neuromorphic hardware, for using quantum physics as an input to classical neural nets and, in turn, for using network results to be fed back to quantum systems. The necessary foundations on both sides, quantum physics and artificial neural networks, are described, providing a valuable reference for researchers from these different communities who need to understand the foundations of both.

Holography: Capturing Depth

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Author :
Publisher : Rob Botwright
ISBN 13 : 1839387270
Total Pages : 198 pages
Book Rating : 4.8/5 (393 download)

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Book Synopsis Holography: Capturing Depth by : Rob Botwright

Download or read book Holography: Capturing Depth written by Rob Botwright and published by Rob Botwright. This book was released on 101-01-01 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: 🌟 Dive into the captivating world of holography with our exclusive book bundle: "Holography: Capturing Depth - Optics, 3D Imaging, and Laser Technology"! 🚀 Unleash your curiosity and embark on an enlightening journey through four compelling volumes that explore the intricate intersections of optics, 3D imaging, and laser technology. 📚 📘 Book 1: "Introduction to Holography: A Beginner's Guide to Optics and Laser Technology" lays the groundwork for your exploration, offering a comprehensive overview of holography's basic principles and its foundation in optics and laser technology. 🌈 📗 In Book 2, "Mastering 3D Imaging: Techniques and Applications in Modern Holography," you'll delve deeper into advanced techniques and diverse applications of holographic imaging, unlocking the secrets behind immersive visual experiences. 🌌 📙 Prepare to be dazzled in Book 3, "Advanced Laser Systems: Exploring Cutting-Edge Technologies for Holographic Displays," where you'll discover the latest advancements driving innovation in holographic display technologies, paving the way for a future of boundless possibilities. 💡 📕 And finally, in Book 4, "Holography Beyond Limits: Expert Insights into Quantum Holographic Principles and Future Frontiers," you'll push the boundaries of holography into the realm of quantum mechanics and emerging technologies, unlocking new realms of understanding and potential. 🔮 🌟 Whether you're a novice seeking to understand the basics or a seasoned expert exploring the forefront of innovation, "Holography: Capturing Depth" is your ultimate guide to unlocking the mysteries of holography and beyond. 🌟 Don't miss out on this incredible opportunity to expand your knowledge and dive into the limitless possibilities of holographic technology! Grab your bundle now and embark on an unforgettable journey! 🚀🔬🌌

Quantum-Like Models for Information Retrieval and Decision-Making

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Author :
Publisher : Springer Nature
ISBN 13 : 3030259137
Total Pages : 173 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Quantum-Like Models for Information Retrieval and Decision-Making by : Diederik Aerts

Download or read book Quantum-Like Models for Information Retrieval and Decision-Making written by Diederik Aerts and published by Springer Nature. This book was released on 2019-09-09 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have been characterized by tremendous advances in quantum information and communication, both theoretically and experimentally. In addition, mathematical methods of quantum information and quantum probability have begun spreading to other areas of research, beyond physics. One exciting new possibility involves applying these methods to information science and computer science (without direct relation to the problems of creation of quantum computers). The aim of this Special Volume is to encourage scientists, especially the new generation (master and PhD students), working in computer science and related mathematical fields to explore novel possibilities based on the mathematical formalisms of quantum information and probability. The contributing authors, who hail from various countries, combine extensive quantum methods expertise with real-world experience in application of these methods to computer science. The problems considered chiefly concern quantum information-probability based modeling in the following areas: information foraging; interactive quantum information access; deep convolutional neural networks; decision making; quantum dynamics; open quantum systems; and theory of contextual probability. The book offers young scientists (students, PhD, postdocs) an essential introduction to applying the mathematical apparatus of quantum theory to computer science, information retrieval, and information processes.

Tensor Network Contractions

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Publisher : Springer Nature
ISBN 13 : 3030344894
Total Pages : 160 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Tensor Network Contractions by : Shi-Ju Ran

Download or read book Tensor Network Contractions written by Shi-Ju Ran and published by Springer Nature. This book was released on 2020-01-27 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tensor network is a fundamental mathematical tool with a huge range of applications in physics, such as condensed matter physics, statistic physics, high energy physics, and quantum information sciences. This open access book aims to explain the tensor network contraction approaches in a systematic way, from the basic definitions to the important applications. This book is also useful to those who apply tensor networks in areas beyond physics, such as machine learning and the big-data analysis. Tensor network originates from the numerical renormalization group approach proposed by K. G. Wilson in 1975. Through a rapid development in the last two decades, tensor network has become a powerful numerical tool that can efficiently simulate a wide range of scientific problems, with particular success in quantum many-body physics. Varieties of tensor network algorithms have been proposed for different problems. However, the connections among different algorithms are not well discussed or reviewed. To fill this gap, this book explains the fundamental concepts and basic ideas that connect and/or unify different strategies of the tensor network contraction algorithms. In addition, some of the recent progresses in dealing with tensor decomposition techniques and quantum simulations are also represented in this book to help the readers to better understand tensor network. This open access book is intended for graduated students, but can also be used as a professional book for researchers in the related fields. To understand most of the contents in the book, only basic knowledge of quantum mechanics and linear algebra is required. In order to fully understand some advanced parts, the reader will need to be familiar with notion of condensed matter physics and quantum information, that however are not necessary to understand the main parts of the book. This book is a good source for non-specialists on quantum physics to understand tensor network algorithms and the related mathematics.

Machine Learning Meets Quantum Physics

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Author :
Publisher : Springer Nature
ISBN 13 : 3030402452
Total Pages : 473 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Machine Learning Meets Quantum Physics by : Kristof T. Schütt

Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

ECAI 2020

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Publisher : IOS Press
ISBN 13 : 164368101X
Total Pages : 3122 pages
Book Rating : 4.6/5 (436 download)

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Book Synopsis ECAI 2020 by : G. De Giacomo

Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Quantum Monte Carlo Approaches for Correlated Systems

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Author :
Publisher : Cambridge University Press
ISBN 13 : 1108547311
Total Pages : 287 pages
Book Rating : 4.1/5 (85 download)

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Book Synopsis Quantum Monte Carlo Approaches for Correlated Systems by : Federico Becca

Download or read book Quantum Monte Carlo Approaches for Correlated Systems written by Federico Becca and published by Cambridge University Press. This book was released on 2017-11-30 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past several decades, computational approaches to studying strongly-interacting systems have become increasingly varied and sophisticated. This book provides a comprehensive introduction to state-of-the-art quantum Monte Carlo techniques relevant for applications in correlated systems. Providing a clear overview of variational wave functions, and featuring a detailed presentation of stochastic samplings including Markov chains and Langevin dynamics, which are developed into a discussion of Monte Carlo methods. The variational technique is described, from foundations to a detailed description of its algorithms. Further topics discussed include optimisation techniques, real-time dynamics and projection methods, including Green's function, reptation and auxiliary-field Monte Carlo, from basic definitions to advanced algorithms for efficient codes, and the book concludes with recent developments on the continuum space. Quantum Monte Carlo Approaches for Correlated Systems provides an extensive reference for students and researchers working in condensed matter theory or those interested in advanced numerical methods for electronic simulation.

Quantum Monte Carlo Approaches for Correlated Systems

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Author :
Publisher : Cambridge University Press
ISBN 13 : 1107129931
Total Pages : 287 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Quantum Monte Carlo Approaches for Correlated Systems by : Federico Becca

Download or read book Quantum Monte Carlo Approaches for Correlated Systems written by Federico Becca and published by Cambridge University Press. This book was released on 2017-11-30 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to state-of-the-art quantum Monte Carlo techniques for applications in strongly-interacting systems. Including variational wave functions, stochastic samplings, the variational technique, optimisation techniques, real-time dynamics and projection methods and recent developments on the continuum space. An extensive resource for students and researchers.

Introduction to Tensor Network Methods

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Author :
Publisher : Springer
ISBN 13 : 3030014096
Total Pages : 172 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Introduction to Tensor Network Methods by : Simone Montangero

Download or read book Introduction to Tensor Network Methods written by Simone Montangero and published by Springer. This book was released on 2018-11-28 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume of lecture notes briefly introduces the basic concepts needed in any computational physics course: software and hardware, programming skills, linear algebra, and differential calculus. It then presents more advanced numerical methods to tackle the quantum many-body problem: it reviews the numerical renormalization group and then focuses on tensor network methods, from basic concepts to gauge invariant ones. Finally, in the last part, the author presents some applications of tensor network methods to equilibrium and out-of-equilibrium correlated quantum matter. The book can be used for a graduate computational physics course. After successfully completing such a course, a student should be able to write a tensor network program and can begin to explore the physics of many-body quantum systems. The book can also serve as a reference for researchers working or starting out in the field.

Tensor Network States and Effective Particles for Low-Dimensional Quantum Spin Systems

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Author :
Publisher : Springer
ISBN 13 : 3319641913
Total Pages : 219 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Tensor Network States and Effective Particles for Low-Dimensional Quantum Spin Systems by : Laurens Vanderstraeten

Download or read book Tensor Network States and Effective Particles for Low-Dimensional Quantum Spin Systems written by Laurens Vanderstraeten and published by Springer. This book was released on 2017-08-10 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis develops new techniques for simulating the low-energy behaviour of quantum spin systems in one and two dimensions. Combining these developments, it subsequently uses the formalism of tensor network states to derive an effective particle description for one- and two-dimensional spin systems that exhibit strong quantum correlations. These techniques arise from the combination of two themes in many-particle physics: (i) the concept of quasiparticles as the effective low-energy degrees of freedom in a condensed-matter system, and (ii) entanglement as the characteristic feature for describing quantum phases of matter. Whereas the former gave rise to the use of effective field theories for understanding many-particle systems, the latter led to the development of tensor network states as a description of the entanglement distribution in quantum low-energy states.

Deep Learning and Physics

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Publisher : Springer Nature
ISBN 13 : 9813361085
Total Pages : 207 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Deep Learning and Physics by : Akinori Tanaka

Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.

Emergent Phenomena in Correlated Matter

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Publisher : Forschungszentrum Jülich
ISBN 13 : 3893368841
Total Pages : 562 pages
Book Rating : 4.8/5 (933 download)

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Book Synopsis Emergent Phenomena in Correlated Matter by : Eva Pavarini

Download or read book Emergent Phenomena in Correlated Matter written by Eva Pavarini and published by Forschungszentrum Jülich. This book was released on 2013 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Interacting Electrons and Quantum Magnetism

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Publisher : Springer Science & Business Media
ISBN 13 : 1461208696
Total Pages : 249 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Interacting Electrons and Quantum Magnetism by : Assa Auerbach

Download or read book Interacting Electrons and Quantum Magnetism written by Assa Auerbach and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the excitement and rapid pace of developments, writing pedagogical texts has low priority for most researchers. However, in transforming my lecture l notes into this book, I found a personal benefit: the organization of what I understand in a (hopefully simple) logical sequence. Very little in this text is my original contribution. Most of the knowledge was collected from the research literature. Some was acquired by conversations with colleagues; a kind of physics oral tradition passed between disciples of a similar faith. For many years, diagramatic perturbation theory has been the major theoretical tool for treating interactions in metals, semiconductors, itiner ant magnets, and superconductors. It is in essence a weak coupling expan sion about free quasiparticles. Many experimental discoveries during the last decade, including heavy fermions, fractional quantum Hall effect, high temperature superconductivity, and quantum spin chains, are not readily accessible from the weak coupling point of view. Therefore, recent years have seen vigorous development of alternative, nonperturbative tools for handling strong electron-electron interactions. I concentrate on two basic paradigms of strongly interacting (or con strained) quantum systems: the Hubbard model and the Heisenberg model. These models are vehicles for fundamental concepts, such as effective Ha miltonians, variational ground states, spontaneous symmetry breaking, and quantum disorder. In addition, they are used as test grounds for various nonperturbative approximation schemes that have found applications in diverse areas of theoretical physics.

Quantum Machine Learning: An Applied Approach

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Publisher : Apress
ISBN 13 : 9781484270974
Total Pages : 551 pages
Book Rating : 4.2/5 (79 download)

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Book Synopsis Quantum Machine Learning: An Applied Approach by : Santanu Ganguly

Download or read book Quantum Machine Learning: An Applied Approach written by Santanu Ganguly and published by Apress. This book was released on 2021-08-11 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author’s active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. What You will Learn Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive Who This Book Is For Data scientists, machine learning professionals, and researchers

Supervised Learning with Quantum Computers

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Author :
Publisher : Springer
ISBN 13 : 3319964240
Total Pages : 293 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Supervised Learning with Quantum Computers by : Maria Schuld

Download or read book Supervised Learning with Quantum Computers written by Maria Schuld and published by Springer. This book was released on 2018-08-30 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Looking Inside Jets

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Publisher : Springer
ISBN 13 : 3030157091
Total Pages : 205 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Looking Inside Jets by : Simone Marzani

Download or read book Looking Inside Jets written by Simone Marzani and published by Springer. This book was released on 2019-05-11 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise primer reviews the latest developments in the field of jets. Jets are collinear sprays of hadrons produced in very high-energy collisions, e.g. at the LHC or at a future hadron collider. They are essential to and ubiquitous in experimental analyses, making their study crucial. At present LHC energies and beyond, massive particles around the electroweak scale are frequently produced with transverse momenta that are much larger than their mass, i.e., boosted. The decay products of such boosted massive objects tend to occupy only a relatively small and confined area of the detector and are observed as a single jet. Jets hence arise from many different sources and it is important to be able to distinguish the rare events with boosted resonances from the large backgrounds originating from Quantum Chromodynamics (QCD). This requires familiarity with the internal properties of jets, such as their different radiation patterns, a field broadly known as jet substructure. This set of notes begins by providing a phenomenological motivation, explaining why the study of jets and their substructure is of particular importance for the current and future program of the LHC, followed by a brief but insightful introduction to QCD and to hadron-collider phenomenology. The next section introduces jets as complex objects constructed from a sequential recombination algorithm. In this context some experimental aspects are also reviewed. Since jet substructure calculations are multi-scale problems that call for all-order treatments (resummations), the bases of such calculations are discussed for simple jet quantities. With these QCD and jet physics ingredients in hand, readers can then dig into jet substructure itself. Accordingly, these notes first highlight the main concepts behind substructure techniques and introduce a list of the main jet substructure tools that have been used over the past decade. Analytic calculations are then provided for several families of tools, the goal being to identify their key characteristics. In closing, the book provides an overview of LHC searches and measurements where jet substructure techniques are used, reviews the main take-home messages, and outlines future perspectives.

Scientific and Technical Aerospace Reports

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Publisher :
ISBN 13 :
Total Pages : 376 pages
Book Rating : 4.:/5 (3 download)

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Book Synopsis Scientific and Technical Aerospace Reports by :

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.