Applications of Machine Learning to Studies of Quantum Phase Transitions PDF Download

Author: Laura Malo Roset
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description
In the past years Machine Learning has shown to be a useful tool in quantum many-body physics to detect phase transitions. Being able to identify phases via machine learning introduces the question of how did the algorithm learn to classify them, and thus how to interpret the model?s prediction. In this thesis we present a study of the transition from a normal insulator to a topological insulator. We study this quantum phase transition in the framework of the Su-Schrie?er-Heeger model. In the area of Deep Learning, we introduce two models, a normal convolutional neural network and a model based on deep residual learning. In particular, we focus on the interpretability of the model and its prediction by generating class activation maps (CAM) using a global average pooling (GAP) layer. We show the application of this technique by applying it on the model without disorder and with disorder. Here we give further analysis of the detection of states using transfer learning from no disordered to disordered systems. We conclude that the neural network is able to detect edge states when there is no disorder but unable to distinguish between edge states and Anderson localized states when disorder is introduced.

Author: Kristof T. Schütt
Publisher: Springer Nature
ISBN: 3030402452
Category : Science
Languages : en
Pages : 473

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Book Description
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: N.B. Singh
Publisher: N.B. Singh
ISBN:
Category : Computers
Languages : en
Pages : 472

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Book Description
"Quantum Data Structures Fundamentals" is a beginner-friendly exploration of the essential concepts underpinning quantum computing. Written for readers with non-mathematical backgrounds and absolute beginners, this book delves into the fundamental principles of quantum data structures, offering clear explanations and intuitive insights. From understanding the basics of qubits and quantum gates to exploring advanced topics such as quantum cryptography and quantum simulation, each chapter provides a comprehensive overview of key concepts in a reader-friendly manner. Through accessible language and practical examples, this book serves as an introductory guide to the fascinating world of quantum computing, empowering readers to grasp foundational concepts and lay the groundwork for further exploration in this rapidly evolving field.

Author: G. De Giacomo
Publisher: IOS Press
ISBN: 164368101X
Category : Computers
Languages : en
Pages : 3122

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Book Description
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: Rob Botwright
Publisher: Rob Botwright
ISBN: 1839387270
Category : Technology & Engineering
Languages : en
Pages : 198

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Book Description
🌟 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: Jiří Mikyška
Publisher: Springer Nature
ISBN: 3031360303
Category : Computers
Languages : en
Pages : 809

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Book Description
The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023. The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks. The theme for 2023, "Computation at the Cutting Edge of Science", highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational and systems biology, environmental systems, finance, and others.

Author: N.B. Singh
Publisher: N.B. Singh
ISBN:
Category : Science
Languages : en
Pages : 226

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Book Description
"Essential Quantum Calculus" is a concise and accessible guide that demystifies quantum calculus, offering readers a fundamental understanding of its principles. This book provides a clear introduction to the mathematical concepts essential for grasping quantum mechanics, making it an indispensable resource for students and enthusiasts seeking a solid foundation in the intricate world of quantum physics

Author: Elena V. Levchenko
Publisher: Springer Nature
ISBN: 3030377903
Category : Technology & Engineering
Languages : en
Pages : 292

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Book Description
This book provides a unique and comprehensive overview of the latest advances, challenges and accomplishments in the rapidly growing field of theoretical and computational materials science. Today, an increasing number of industrial communities rely more and more on advanced atomic-scale methods to obtain reliable predictions of materials properties, complement qualitative experimental analyses and circumvent experimental difficulties. The book examines some of the latest and most advanced simulation techniques currently available, as well as up-to-date theoretical approaches adopted by a selected panel of twelve international research teams. It covers a wide range of novel and advanced materials, exploring their structural, elastic, optical, mass and electronic transport properties. The cutting-edge techniques presented appeal to physicists, applied mathematicians and engineers interested in advanced simulation methods in materials science. The book can also be used as additional literature for undergraduate and postgraduate students with majors in physics, chemistry, applied mathematics and engineering.

Author: Stefanie Czischek
Publisher: Springer Nature
ISBN: 3030527158
Category : Science
Languages : en
Pages : 205

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Book Description
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: Maria Schuld
Publisher: Springer
ISBN: 3319964240
Category : Science
Languages : en
Pages : 293

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Book Description
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.