Neural Network Simulation Of Strongly Correlated Quantum Systems

Author : Stefanie Czischek
Publisher : Springer Nature
Page : 205 pages
File Size : 49,6 Mb
Release : 2020-08-27
Category : Science
ISBN : 9783030527150

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Neural-Network Simulation of Strongly Correlated Quantum Systems by Stefanie Czischek Pdf

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.

Author : Rob Botwright
Publisher : Rob Botwright
Page : 198 pages
File Size : 53,5 Mb
Release : 101-01-01
Category : Technology & Engineering
ISBN : 9781839387272

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Holography: Capturing Depth by Rob Botwright Pdf

🌟 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! 🚀🔬🌌

Author : Shi-Ju Ran,Emanuele Tirrito,Cheng Peng,Xi Chen,Luca Tagliacozzo,Gang Su,Maciej Lewenstein
Publisher : Springer Nature
Page : 160 pages
File Size : 50,7 Mb
Release : 2020-01-27
Category : Science
ISBN : 9783030344894

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Tensor Network Contractions by Shi-Ju Ran,Emanuele Tirrito,Cheng Peng,Xi Chen,Luca Tagliacozzo,Gang Su,Maciej Lewenstein Pdf

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.

Author : Diederik Aerts,Andrei Khrennikov,Massimo Melucci,Bourama Toni
Publisher : Springer Nature
Page : 173 pages
File Size : 48,8 Mb
Release : 2019-09-09
Category : Science
ISBN : 9783030259136

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Quantum-Like Models for Information Retrieval and Decision-Making by Diederik Aerts,Andrei Khrennikov,Massimo Melucci,Bourama Toni Pdf

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.

Author : G. De Giacomo,A. Catala,B. Dilkina
Publisher : IOS Press
Page : 3122 pages
File Size : 49,5 Mb
Release : 2020-09-11
Category : Computers
ISBN : 9781643681016

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ECAI 2020 by G. De Giacomo,A. Catala,B. Dilkina Pdf

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.

Author : Federico Becca,Sandro Sorella
Publisher : Cambridge University Press
Page : 287 pages
File Size : 42,9 Mb
Release : 2017-11-30
Category : Science
ISBN : 9781107129931

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Quantum Monte Carlo Approaches for Correlated Systems by Federico Becca,Sandro Sorella Pdf

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.

Author : Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller
Publisher : Springer Nature
Page : 473 pages
File Size : 45,9 Mb
Release : 2020-06-03
Category : Science
ISBN : 9783030402457

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Machine Learning Meets Quantum Physics by Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller Pdf

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.

Author : Santanu Ganguly
Publisher : Apress
Page : 551 pages
File Size : 40,9 Mb
Release : 2021-08-11
Category : Computers
ISBN : 1484270975

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Quantum Machine Learning: An Applied Approach by Santanu Ganguly Pdf

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

Author : Adolfo Avella,Ferdinando Mancini
Publisher : Springer
Page : 0 pages
File Size : 50,6 Mb
Release : 2016-09-22
Category : Science
ISBN : 3662505932

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Strongly Correlated Systems by Adolfo Avella,Ferdinando Mancini Pdf

The continuous evolution and development of experimental techniques is at the basis of any fundamental achievement in modern physics. Strongly correlated systems (SCS), more than any other, need to be investigated through the greatest variety of experimental techniques in order to unveil and crosscheck the numerous and puzzling anomalous behaviors characterizing them. The study of SCS fostered the improvement of many old experimental techniques, but also the advent of many new ones just invented in order to analyze the complex behaviors of these systems. Many novel materials, with functional properties emerging from macroscopic quantum behaviors at the frontier of modern research in physics, chemistry and materials science, belong to this class of systems. The volume presents a representative collection of the modern experimental techniques specifically tailored for the analysis of strongly correlated systems. Any technique is presented in great detail by its own inventor or by one of the world-wide recognized main contributors. The exposition has a clear pedagogical cut and fully reports on the most relevant case study where the specific technique showed to be very successful in describing and enlightening the puzzling physics of a particular strongly correlated system. The book is intended for advanced graduate students and post-docs in the field as textbook and/or main reference, but also for any other researcher in the field who appreciates consulting a single, but comprehensive, source or wishes to get acquainted, in a as painless as possible way, with the working details of a specific technique.

Author : Anonim
Publisher : Unknown
Page : 376 pages
File Size : 53,8 Mb
Release : 1995
Category : Aeronautics
ISBN : MINN:30000011064601

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Scientific and Technical Aerospace Reports by Anonim Pdf

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.

Author : Gi-Chul Yang,Sio-Iong Ao,Len Gelman
Publisher : Springer Science & Business
Page : 700 pages
File Size : 40,9 Mb
Release : 2014-04-26
Category : Technology & Engineering
ISBN : 9789401788328

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Transactions on Engineering Technologies by Gi-Chul Yang,Sio-Iong Ao,Len Gelman Pdf

This book contains revised and extended research articles written by prominent researchers participating in the international conference on Advances in Engineering Technologies and Physical Science (London, U.K., 3-5 July, 2013). Topics covered include mechanical engineering, bioengineering, internet engineering, image engineering, wireless networks, knowledge engineering, manufacturing engineering, and industrial applications. The book offers state of art of tremendous advances in engineering technologies and physical science and applications, and also serves as an excellent reference work for researchers and graduate students working with/on engineering technologies and physical science.

Author : Anonim
Publisher : Unknown
Page : 1144 pages
File Size : 51,8 Mb
Release : 2006
Category : Physics
ISBN : UOM:39015059004088

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Journal of the Physical Society of Japan by Anonim Pdf

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 51,6 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Author : Eva Pavarini,Erik Koch,Ulrich Schollwöck
Publisher : Forschungszentrum Jülich
Page : 562 pages
File Size : 42,8 Mb
Release : 2013
Category : Electronic
ISBN : 9783893368846

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Emergent Phenomena in Correlated Matter by Eva Pavarini,Erik Koch,Ulrich Schollwöck Pdf

Author : Akinori Tanaka,Akio Tomiya,Koji Hashimoto
Publisher : Springer Nature
Page : 207 pages
File Size : 51,5 Mb
Release : 2021-03-24
Category : Science
ISBN : 9789813361089

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Deep Learning and Physics by Akinori Tanaka,Akio Tomiya,Koji Hashimoto Pdf

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.