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Probability for Deep Learning Quantum
Probability for Deep Learning Quantum provides readers with the first book to address probabilistic methods in the deep learning environment and the quantum technological area simultaneously, by using a common platform: the Many-Sorted Algebra (MSA) view. While machine learning is created with a foundation of probability, probability is at the heart of quantum physics as well. It is the cornerstone in quantum applications. These applications include quantum measuring, quantum information theory, quantum communication theory, quantum sensing, quantum signal processing, quantum computing, quantum cryptography, and quantum machine learning. Although some of the probabilistic methods differ in machine learning disciplines from those in the quantum technologies, many techniques are very similar. Probability is introduced in the text rigorously, in Komogorov’s vision. It is however, slightly modified by developing the theory in a Many-Sorted Algebra setting. This algebraic construct is also used in showing the shared structures underlying much of both machine learning and quantum theory. Both deep learning and quantum technologies have several probabilistic and stochastic methods in common. These methods are described and illustrated using numerous examples within the text. Concepts in entropy are provided from a Shannon as well as a von-Neumann view. Singular value decomposition is applied in machine learning as a basic tool and presented in the Schmidt decomposition. Besides the in-common methods, Born’s rule as well as positive operator valued measures are described and illustrated, along with quasi-probabilities. Author Charles R. Giardina provides clear and concise explanations, accompanied by insightful and thought-provoking visualizations, to deepen your understanding and enable you to apply the concepts to real-world scenarios.
R 5,297.00
Quantum Stochastics
The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigroups and processes, and large-time asymptotic behavior of quantum Markov semigroups.
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Quantum Probability And Related Topics: Volume Viii
Quantum Probability and Related Topics is a series of volumes based on material discussed at the various QP conferences. It aims to provide an update on the rapidly growing field of classical probability, quantum physics and functional analysis.
R 2,494.55
Quantum Probability And Related Topics: Volume Viii
Quantum Probability and Related Topics is a series of volumes based on material discussed at the various QP conferences. It aims to provide an update on the rapidly growing field of classical probability, quantum physics and functional analysis.
R 5,834.17
Infinite Dimensional Analysis, Quantum Probability and Applications
This proceedings volume gathers selected, peer-reviewed papers presented at the 41st International Conference on Infinite Dimensional Analysis, Quantum Probability and Related Topics (QP41) that was virtually held at the United Arab Emirates University (UAEU) in Al Ain, Abu Dhabi, from March 28th to April 1st, 2021. The works cover recent developments in quantum probability and infinite dimensional analysis, with a special focus on applications to mathematical physics and quantum information theory. Covered topics include white noise theory, quantum field theory, quantum Markov processes, free probability, interacting Fock spaces, and more. By emphasizing the interconnection and interdependence of such research topics and their real-life applications, this reputed conference has set itself as a distinguished forum to communicate and discuss new findings in truly relevant aspects of theoretical and applied mathematics, notably in the field of mathematical physics, as well as an event of choice for the promotion of mathematical applications that address the most relevant problems found in industry. That makes this volume a suitable reading not only for researchers and graduate students with an interest in the field but for practitioners as well.
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Quantum Probability and Applications III
These proceedings of the first Quantum Probability meeting held in Oberwolfach is the fourth in a series begun with the 1982 meeting of Mondragone and continued in Heidelberg ('84) and in Leuven ('85). The main topics discussed were: quantum stochastic calculus, mathematical models of quantum noise and their applications to quantum optics, the quantum Feynman-Kac formula, quantum probability and models of quantum statistical mechanics, the notion of conditioning in quantum probability and related problems (dilations, quantum Markov processes), quantum central limit theorems. With the exception of Kümmerer's review article on Quantum Markov Processes, all contributions are original research papers.
R 1,702.96
Quantum Foundations, Probability and Information
Composed of contributions from leading experts in quantum foundations, this volume presents viewpoints on a number of complex problems through informational, probabilistic, and mathematical perspectives and features novel mathematical models of quantum and subquantum phenomena. Rich with multi-disciplinary mathematical content, this book includes applications of partial differential equations in quantum field theory, differential geometry, oscillatory processes and vibrations, and Feynman integrals for quickly growing potential functions. Due to rapid growth in the field in recent years, this volume aims to promote interdisciplinary collaboration in the areas of quantum probability, information, communication and foundation, and mathematical physics. Many papers discuss complex yet novel problems that depart from the mainstream of quantum physical studies. Others devote explanation to fundamental problems of the conventional quantum theory, including its mathematical formalism. Overall, authors cover a diverse set of topics, including quantum and classical field theory and oscillatory processing, quantum mechanics from a Darwinian evolutionary perspective, and biological applications of quantum theory.Together in one volume, these essays will be useful to experts in the corresponding areas of quantum theory. Theoreticians, experimenters, mathematicians, and even philosophers in quantum physics and quantum probability and information theory can consider this book a valuable resource.
R 4,396.00
Quantum Machine Learning
This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies.In summary, the book: Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects.This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.
R 2,674.00
Foundations of Probability with Applications
This is an important collection of essays dealing with the foundations of probability that will be of value to philosophers of science, mathematicians, statisticians, psychologists and educationalists. The collection falls into three parts. Part I comprises five essays on the axiomatic foundations of probability. Part II contains seven articles on probabilistic causality and quantum mechanics, with an emphasis on the existence of hidden variables. The third part consists of a single extended essay applying probabilistic theories of learning to practical questions of education: it incorporates extensive data analysis. Patrick Suppes is one of the world's foremost philosophers in the area of probability, and has made many contributions to both the theoretical and practical side of education. The statistician Mario Zanotti is a long-time collaborator.
R 4,356.91
Infinite Dimensional Analysis, Quantum Probability and Applications
This proceedings volume gathers selected, peer-reviewed papers presented at the 41st International Conference on Infinite Dimensional Analysis, Quantum Probability and Related Topics (QP41) that was virtually held at the United Arab Emirates University (UAEU) in Al Ain, Abu Dhabi, from March 28th to April 1st, 2021. The works cover recent developments in quantum probability and infinite dimensional analysis, with a special focus on applications to mathematical physics and quantum information theory. Covered topics include white noise theory, quantum field theory, quantum Markov processes, free probability, interacting Fock spaces, and more. By emphasizing the interconnection and interdependence of such research topics and their real-life applications, this reputed conference has set itself as a distinguished forum to communicate and discuss new findings in truly relevant aspects of theoretical and applied mathematics, notably in the field of mathematical physics, as well as an event of choice for the promotion of mathematical applications that address the most relevant problems found in industry. That makes this volume a suitable reading not only for researchers and graduate students with an interest in the field but for practitioners as well.
R 6,228.00
Quantum Probability and Spectral Analysis of Graphs
This is the first book to comprehensively cover quantum probabilistic approaches to spectral analysis of graphs, an approach developed by the authors. The book functions as a concise introduction to quantum probability from an algebraic aspect. Here readers will learn several powerful methods and techniques of wide applicability, recently developed under the name of quantum probability. The exercises at the end of each chapter help to deepen understanding.
R 3,052.61
Quantum Foundations, Probability and Information
Composed of contributions from leading experts in quantum foundations, this volume presents viewpoints on a number of complex problems through informational, probabilistic, and mathematical perspectives and features novel mathematical models of quantum and subquantum phenomena. Rich with multi-disciplinary mathematical content, this book includes applications of partial differential equations in quantum field theory, differential geometry, oscillatory processes and vibrations, and Feynman integrals for quickly growing potential functions. Due to rapid growth in the field in recent years, this volume aims to promote interdisciplinary collaboration in the areas of quantum probability, information, communication and foundation, and mathematical physics. Many papers discuss complex yet novel problems that depart from the mainstream of quantum physical studies. Others devote explanation to fundamental problems of the conventional quantum theory, including its mathematical formalism. Overall, authors cover a diverse set of topics, including quantum and classical field theory and oscillatory processing, quantum mechanics from a Darwinian evolutionary perspective, and biological applications of quantum theory.Together in one volume, these essays will be useful to experts in the corresponding areas of quantum theory. Theoreticians, experimenters, mathematicians, and even philosophers in quantum physics and quantum probability and information theory can consider this book a valuable resource.
R 4,396.00
Recent Developments in Infinite-Dimensional Analysis and Quantum Probability
Recent Developments in Infinite-Dimensional Analysis and Quantum Probability is dedicated to Professor Takeyuki Hida on the occasion of his 70th birthday. The book is more than a collection of articles. In fact, in it the reader will find a consistent editorial work, devoted to attempting to obtain a unitary picture from the different contributions and to give a comprehensive account of important recent developments in contemporary white noise analysis and some of its applications. For this reason, not only the latest results, but also motivations, explanations and connections with previous work have been included. The wealth of applications, from number theory to signal processing, from optimal filtering to information theory, from the statistics of stationary flows to quantum cable equations, show the power of white noise analysis as a tool. Beyond these, the authors emphasize its connections with practically all branches of contemporary probability, including stochastic geometry, the structure theory of stationary Gaussian processes, Neumann boundary value problems, and large deviations.
R 3,065.36
Quantum Probability And Related Topics - Proceedings Of The 30th Conference
This volume contains current work at the frontiers of research in quantum probability, infinite dimensional stochastic analysis, quantum information and statistics. It presents a carefully chosen collection of articles by experts to highlight the latest developments in those fields. Included in this volume are expository papers which will help increase communication between researchers working in these areas. The tools and techniques presented here will be of great value to research mathematicians, graduate students and applied mathematicians.
R 4,660.46
Quantum Probability and Applications V
These proceedings of the workshop on quantum probability held in Heidelberg, September 26-30, 1988 contains a representative selection of research articles on quantum stochastic processes, quantum stochastic calculus, quantum noise, geometry, quantum probability, quantum central limit theorems and quantum statistical mechanics.
R 1,980.28
Quantum Probability for Probabilists
In recent years, the classical theory of stochastic integration and stochastic differential equations has been extended to a non-commutative set-up to develop models for quantum noises. The author, a specialist of classical stochastic calculus and martingale theory, tries to provide an introduction to this rapidly expanding field in a way which should be accessible to probabilists familiar with the Ito integral. It can also, on the other hand, provide a means of access to the methods of stochastic calculus for physicists familiar with Fock space analysis. For this second edition, the author has added about 30 pages of new material, mostly on quantum stochastic integrals.
R 2,931.00
Recent Trends in Modelling the Continuous Time Series using Deep Learning
This book presents the first unified, practical framework for continuous-time series analysis using state-of-the-art neural architectures. Moving beyond traditional discrete-time methods, it directly addresses real-world challenges such as irregular sampling, asynchronous observations, and hidden system dynamics through Neural ODEs, SDEs, and CDEs. Covering both foundational and advanced models -- RNNs, Transformers, graph networks, and emerging quantum-hybrid approaches -- the book bridges classical time-series theory with modern deep learning. It emphasizes probabilistic forecasting, uncertainty quantification, and cutting-edge generative techniques, including diffusion models and VAEs, equipping readers with tools for robust, interpretable predictions. Recent Trends in Modelling the Continuous Time Series using Deep Learning tackles core issues such as long-range dependencies, multivariate interactions, dimensionality reduction, and spatiotemporal coherence, while providing structured evaluation frameworks and benchmarking protocols tailored to continuous-time settings. Through rich case studies in healthcare (EHR analytics, wearable monitoring), finance (volatility forecasting, high-frequency trading), and IoT systems (sensor fusion, predictive maintenance), the book demonstrates how continuous-time models enable personalized insights, constraint-aware learning, and more reliable decision-making. Designed for researchers, engineers, and practitioners, this book is a definitive resource for applying continuous-time neural methods to complex, real-world environments.
R 7,579.07
Quantum Probability and Spectral Analysis of Graphs
This is the first book to comprehensively cover quantum probabilistic approaches to spectral analysis of graphs, an approach developed by the authors. The book functions as a concise introduction to quantum probability from an algebraic aspect. Here readers will learn several powerful methods and techniques of wide applicability, recently developed under the name of quantum probability. The exercises at the end of each chapter help to deepen understanding.
R 3,052.45
Quantum Probability And Related Topics - Proceedings Of The 32nd Conference
This volume contains the current research in quantum probability, infinite dimensional analysis and related topics. Contributions by experts in these fields highlight the latest developments and interdisciplinary connections with classical probability, stochastic analysis, white noise analysis, functional analysis and quantum information theory.This diversity shows how research in quantum probability and infinite dimensional analysis is very active and strongly involved in the modern mathematical developments and applications.Tools and techniques presented here will be of great value to researchers.
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New Approach To Quantum Theory: The Central Role Of Probability
This book presents an advanced-level reorganization of the probability theory of quantum theory based on multiple quantum events in terms of consecutive and conditional probability. This leads to the recognition that the fundamental quantum time evolution equation, instead of the Schrödinger's equation, is the Generalized Born's Rule, which has a valid (though secondary) role in one model of the axioms of quantum theory usually called the Schrödinger picture. So, this approach is fully consistent with textbook quantum theory. This new approach clarifies the 'paradoxes' in quantum theory of entanglement and collapse. There are few references to classical physics, given that this is a new stand-alone axiomatic presentation of quantum theory. Notice that this is not an 'interpretation' of quantum theory as that word is usually understood. The intended audience consists of scientists, philosophers and students who preferably have some previous knowledge of quantum theory.
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Quantum Machine Learning
This text presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, towards improving traditional machine learning algorithms through case-studies.• Covers the core and fundamental aspects of statistics, quantum learning and quantum machines• Discusses the basics of machine learning, regression, supervised and un-supervised machine learning algorithms, and artificial neural networks• Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, boosting.• Introduces quantum evaluation models, deep quantum learning, ensembles and QBoost• Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspectsThe text is primarily written for scholars and researchers working in the fields of Computer Science and Engineering, Information Technology, Electrical Engineering, and Electronics and Communication Engineering.
R 5,644.78
Quantum Machine Learning: An Applied Approach
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 LearnUnderstand and explore quantum computing and quantum machine learning, and their application in science and industryExplore various data training models utilizing quantum machine learning algorithms and Python librariesGet hands-on and familiar with applied quantum computing, including freely available cloud-based accessBe familiar with techniques for training and scaling quantum neural networksGain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep diveWho This Book Is ForData scientists, machine learning professionals, and researchers
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Machine Learning Theory and Applications
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much moreClassical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs)Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related dataFeature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applicationsMachine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
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Probabilistic Methods in Quantum Field Theory and Quantum Gravity
From August 21 through August 27, 1989 the Nato Advanced Research Workshop Probabilistic Methods in Quantum Field Theory and Quantum Gravity" was held at l'Institut d'Etudes Scientifiques, Cargese, France. This publication is the Proceedings of this workshop. The purpose of the workshop was to bring together a group of scientists who have been at the forefront of the development of probabilistic methods in Quantum Field Theory and Quantum Gravity. The original thought was to put emphasis on the introduction of stochastic processes in the understanding of Euclidean Quantum Field Theory, with also some discussion of recent progress in the field of stochastic numerical methods. During the final preparation of the meeting we broadened the scope to include all those Euclidean Quantum Field Theory descriptions that make direct reference to concepts from probability theory and statistical mechanics. Several of the main contributions centered around a more rigorous discussion of stochastic processes for the formulation of Euclidean Quantum Field Theory. These rather stringent mathematical approaches were contrasted with the more heuristic stochastic quantization scheme developed in 1981 by Parisi and Wu: Stochastic quan tization, its intrinsic BRST -structure and stochastic regularization appeared in many disguises and in connection with several different problems throughout the workshop.
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Foundations of Deep Learning
Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing. The excellent generalizability of deep learning is like a “cloud” to conventional complexity-based learning theory: the over-parameterization of deep learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying deep learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues. The efforts to understand the excellent generalizability are following two major paths: (1) developing size-independent complexity measures, which can evaluate the “effective” hypothesis complexity that can be learned, instead of the whole hypothesis space; and (2) modelling the learned hypothesis through stochastic gradient methods, the dominant optimizers in deep learning, via stochastic differential functions and the geometry of the associated loss functions. Related works discover that over-parameterization surprisingly bring many good properties to the loss functions. Rising concerns of deep learning are seen on the ethical and security issues, including privacy preservation and adversarial robustness. Related works also reveal an interplay between them and generalizability: a good generalizability usually means a good privacy-preserving ability; and more robust algorithms might have a worse generalizability. We expect readers can have a big picture of the current knowledge in deep learning theory, understand how the deep learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.
R 6,834.01
Probability for Statistics and Machine Learning
This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance.This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.
R 3,658.25
Quantum Probability Theory, Psychology and Law
This book provides the first consideration of quantum principles in legal decision making. It shows how quantum probability theory can be applied to psychology and law to deepen our understanding of psychological and legal issues, suggesting a paradigm shift in the study of these areas.Written as an introduction to quantum probability theory for non-physicists, the book offers an accessible summary of how psychology can be applied to quantum phenomena for human decision making. It introduces psychologists and lawyers to fresh conceptual frameworks, including classical probability theory, classical statistics, data mining and quantum theory. Wojciechowski shows how quantum theory and concepts can be transferred to psychological research models, and can help to resolve previously unexplained psychological phenomena, influence scientific views, undermine traditional foundations of the legal system and result in the development of new research directions. Studies conducted by the author are used to show how problems facing legal decision making can be addressed in a completely new light with the application of quantum probability theory.This cutting-edge and innovative book will be of great interest to graduate students and researchers in the fields of forensic psychology and legal decision making, as well as legal professionals and legal scholars.
R 9,297.48
Foundations of Probability with Applications
This is an important collection of essays dealing with the foundations of probability that will be of value to philosophers of science, mathematicians, statisticians, psychologists and educationalists. The collection falls into three parts. Part I comprises five essays on the axiomatic foundations of probability. Part II contains seven articles on probabilistic causality and quantum mechanics, with an emphasis on the existence of hidden variables. The third part consists of a single extended essay applying probabilistic theories of learning to practical questions of education: it incorporates extensive data analysis. Patrick Suppes is one of the world's foremost philosophers in the area of probability, and has made many contributions to both the theoretical and practical side of education. The statistician Mario Zanotti is a long-time collaborator.
R 2,251.40
Interpretations of Probability
01/07 This title is now available from Walter de Gruyter. Please see www.degruyter.com for more information.Like geometry, probability can not be reduced to just one model to describe all physical and biological phenomena. Each model has a restricted range of applications. Quantum physics demonstrated that the use of conventional probability models induces some paradoxes. Such paradoxes can be resolved by using non-Kolmogorov probability models, developed on the basis of purely classical interpretations of probability: frequency and ensemble. Frequency models describe violations of the law of large numbers. Ensemble models are models with infinitely small probabilities. This is the first fundamental book devoted to non-Kolmogorov probability models. It provides the first mathematical theory of negative probabilities - with numerous applications to quantum physics, information theory, complexity, biology and psychology. Natural models with negative (frequency and ensemble) probabilities are developed in the framework of so called p-adic analysis. The book also contains an extremely interesting model of cognitive information reality with flows of information probabilities, describing the process of thinking, social and psychological phenomena. This book will be of value and interest to specialists in probability theory, statistics, functional analysis, quantum physics and (partly) specialists in cognitive sciences and psychology.
R 7,660.26
Gentle Introduction to Quantum Machine Learning
Quantum machine learning (QML) is revolutionizing artificial intelligence by leveraging the power of quantum computing to access previously unimaginable computational possibilities. However, the field remains fragmented--balancing rigorous quantum theory with practical AI applications remains a challenge. This book bridges this gap, offering a systematic, hands-on guide for AI researchers, ML practitioners, and computer scientists eager to explore this emerging frontier. It provides a cohesive roadmap, covering everything from fundamental quantum computing principles to state-of-the-art QML techniques. Readers will explore quantum kernel methods, quantum neural networks, and quantum Transformers, gaining insight into their theoretical foundations, performance advantages, and practical implementations. The book's code demonstrations offer hands-on experience, ensuring that readers can move beyond theory to real-world applications. Designed for those with an AI or ML background, this tutorial does not assume prior expertise in quantum computing. Instead, it presents complex concepts with clarity, making it an essential resource for researchers, graduate students, and industry professionals eager to stay ahead in the quantum AI revolution. Whether you seek to understand quantum speedups, develop quantum-based models, or explore future research directions, this book provides the foundation you need to engage with QML and shape the future of intelligent computing.
R 1,026.00
Recent Developments in Infinite-Dimensional Analysis and Quantum Probability
Recent Developments in Infinite-Dimensional Analysis and Quantum Probability is dedicated to Professor Takeyuki Hida on the occasion of his 70th birthday. The book is more than a collection of articles. In fact, in it the reader will find a consistent editorial work, devoted to attempting to obtain a unitary picture from the different contributions and to give a comprehensive account of important recent developments in contemporary white noise analysis and some of its applications. For this reason, not only the latest results, but also motivations, explanations and connections with previous work have been included. The wealth of applications, from number theory to signal processing, from optimal filtering to information theory, from the statistics of stationary flows to quantum cable equations, show the power of white noise analysis as a tool. Beyond these, the authors emphasize its connections with practically all branches of contemporary probability, including stochastic geometry, the structure theory of stationary Gaussian processes, Neumann boundary value problems, and large deviations.
R 3,066.25
Quantum Probability — Quantum Logic
This book compares various approaches to the interpretation of quantum mechanics, in particular those which are related to the key words "the Copenhagen interpretation", "the antirealist view", "quantum logic" and "hidden variable theory". Using the concept of "correlation" carefully analyzed in the context of classical probability and in quantum theory, the author provides a framework to compare these approaches. He also develops an extension of probability theory to construct a local hidden variable theory. The book should be of interest for physicists and philosophers of science interested in the foundations of quantum theory.
R 1,623.05
Advanced Classical and Quantum Probability Theory with Quantum Field Theory Applications
This book is based on three undergraduate and postgraduate courses taught by the author on Matrix theory, Probability theory and Antenna theory over the past several years.It discusses Matrix theory, Probability theory and Antenna theory with solved problems. It will be useful to undergraduate and postgraduate students of Electronics and Communications Engineering.Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan and Bhutan).
R 2,755.60
Trustworthy Machine Learning under Imperfect Data
The subject of this book centresaround trustworthy machine learning under imperfect data. It is primarily designed forscientists, researchers, practitioners, professionals, postgraduates andundergraduates in thefield of machine learning and artificial intelligence. The book focuseson trustworthy deep learning under various types of imperfect data, includingnoisy labels, adversarial examples, and out-of-distribution data. It coverstrustworthy machine learning algorithms, theories, and systems. The main goal of the book is to provide students and researchers in academia with anunbiased and comprehensive literature review. More importantly, it aims to stimulateinsightful discussions about the future of trustworthy machine learning. By engaging the audiencein more in-depth conversations, the book intends to spark ideas for addressing coreproblems in this topic. For example, it will explore how to build up benchmark datasets innoisy-supervised learning, how to tackle the emerging adversarial learning, andhow to tackle out-of-distribution detection. For practitioners in the industry, this book will present state-of-the-art trustworthy machine learning methods tohelp them solve real-world problems in different scenarios, such as onlinerecommendation and web search. While the book will introduce the basics ofknowledge required, readers will benefit from having some familiarity withlinear algebra, probability, machine learning, and artificial intelligence. Theemphasis will be on conveying the intuition behind all formal concepts, theories, and methodologies, ensuring the book remains self-contained at a highlevel.
R 7,583.62
XIV Symposium on Probability and Stochastic Processes
This volume features a collection of papers and lecture notes from the "XIV Symposium on Probability and Stochastic Processes," held at the Center for Research in Mathematics (CIMAT), Mexico, in November 2023. Contributions discuss state-of-the-art developments in these fields, encouraging an exchange of ideas between international experts in related disciplines. Specific topics covered include: Bienaymé-Galton-Watson trees Infinite-mode Boson-Gaussian states Mean-field games The Deep Linear Network
R 9,069.16
Enhancing Deep Learning with Bayesian Inference
Develop Bayesian Deep Learning models to help make your own applications more robust.Key FeaturesGain insights into the limitations of typical neural networksAcquire the skill to cultivate neural networks capable of estimating uncertaintyDiscover how to leverage uncertainty to develop more robust machine learning systemsBook DescriptionDeep learning is revolutionizing our lives, impacting content recommendations and playing a key role in mission- and safety-critical applications. Yet, typical deep learning methods lack awareness about uncertainty. Bayesian deep learning offers solutions based on approximate Bayesian inference, enhancing the robustness of deep learning systems by indicating how confident they are in their predictions. This book will guide you in incorporating model predictions within your applications with care.Starting with an introduction to the rapidly growing field of uncertainty-aware deep learning, you’ll discover the importance of uncertainty estimation in robust machine learning systems. You’ll then explore a variety of popular Bayesian deep learning methods and understand how to implement them through practical Python examples covering a range of application scenarios.By the end of this book, you’ll embrace the power of Bayesian deep learning and unlock a new level of confidence in your models for safer, more robust deep learning systems.What you will learnDiscern the advantages and disadvantages of Bayesian inference and deep learningBecome well-versed with the fundamentals of Bayesian Neural NetworksUnderstand the differences between key BNN implementations and approximationsRecognize the merits of probabilistic DNNs in production contextsMaster the implementation of a variety of BDL methods in Python codeApply BDL methods to real-world problemsEvaluate BDL methods and choose the most suitable approach for a given taskDevelop proficiency in dealing with unexpected data in deep learning applicationsWho this book is forThis book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You’re expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.
R 2,052.00
Quantum-Like Models for Information Retrieval and Decision-Making
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.
R 6,551.19
Quantum Machine Learning with Python
Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others.You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research.What You'll LearnUnderstand Quantum computing and Quantum machine learningExplore varied domains and the scenarios where Quantum machine learning solutions can be appliedDevelop expertise in algorithm development in varied Quantum computing frameworksReview the major challenges of building large scale Quantum computers and applying its various techniquesWho This Book Is ForMachine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning
R 1,254.00
Deep Learning
Deep Learning: From Algorithmic Essence to Industrial Practice introduces the fundamental theories of deep learning, engineering practices, and their deployment and application in the industry. This book provides a detailed explanation of classic convolutional neural networks, recurrent neural networks, and transformer networks based on self-attention mechanisms, along with their variants, combining code demonstrations. Additionally, this book covers the applications of these models in areas including image classification, object detection, and semantic segmentation. This book also considers advancements in deep reinforcement learning and generative adversarial networks making it suitable for graduate and senior undergraduate students with backgrounds in computer science, automation, electronics, communications, mathematics, and physics, as well as professional technical personnel who wish to work or are preparing to transition into the field of artificial intelligenceThe code for book may be accessed by visiting the companion website: https://www.elsevier.com/books-and-journals/book-companion/9780443439544
R 7,062.00
Quantum Probability and Applications IV
This volume, the fourth of the quantum probability series, collects part of the contributions to the Year of Quantum Probability organized by the Volterra Center of University of Rome II. The intensive communication among researchers during this Year allowed several open problems to be solved and several inexpected connections to be revealed.
R 1,325.67
Applications of Quantum Field Theory to Problems in Machine Learning
This book examines quantum neural networks through renormalization techniques, supersymmetric field theory, and noisy harmonic oscillator systems. The book's analysis covers adaptive beamforming applications, brain modeling, gravitational control mechanisms, and mixed-state dynamics in superstring theory, and also includes:Comprehensive analysis of quantum neural networks through renormalization techniques and supersymmetric field theory applications in computational modelingInvestigation of quantum field dynamics with noise integration, filtering mechanisms, and scattering processes in curved spacetime environmentsStudy of adaptive beamforming methodologies combined with quantum neural networks for brain modeling and evolving field system applicationsExamination of mixed-state dynamics in superstring theory frameworks with emphasis on quantum noisy fields and supersymmetric effectsAnalysis of extended Kalman filter integration with quantum neural networks for transmission line control and field estimation optimizationThe work explores extended Kalman filter methodologies for transmission line control, field estimation, and symmetry-broken dynamics in signal processing systems for advanced computational modeling applications.This title has been co-published with Manakin Press. T&F does not sell or distribute the print editions in Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri lanka.
R 9,158.00
Quantum Probability And Infinite Dimensional Analysis - Proceedings Of The 29th Conference
This is the proceedings of the 29th Conference on Quantum Probability and Infinite Dimensional Analysis, which was held in Hammamet, Tunisia.
R 5,572.31
Quantum-Like Models for Information Retrieval and Decision-Making
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.
R 6,761.11
Quantum Machine Learning
In the exploration of new frontiers in data-driven solutions, the potential of quantum-enhanced machine learning has become too important to overlook. Quantum machine learning, though still in its formative stages, holds the promise to tackle some of the most complex problems that lie beyond the reach of classical computing. Quantum Machine Learning: Concepts, Algorithms, and Applications is a guide to understanding such quantum principles as superposition and entanglement and how they can enhance learning algorithms and data-processing capabilities. The book features a carefully structured progression from foundational concepts and core algorithms to application-driven case studies and emerging directions for future exploration.The book provides a broad and in-depth treatment of topics ranging from quantum data encoding and quantum neural networks to hybrid models and optimization frameworks. Emphasis has also been placed on real-world use cases and the practical tools available for implementation, thereby ensuring that this book serves not only as a reference but also as a springboard for experimentation and innovation. Highlights include the following:Implementing quantum neural networks on near-term quantum hardwareQuantum variational optimization for machine learningQuantum-accelerated neural imputations with large language modelsEmerging trends, addressing hardware limitations, algorithm optimization, and ethical considerations.This book serves as both a primer and an advanced guide by providing essential knowledge for understanding and implementing quantum-enhanced AI solutions in various professional contexts. It equips readers to become active participants in the quantum revolution transforming machine learning.
R 10,257.00
