Search results for: quantum-machine-learning

Quantum Machine Learning

Author : Siddhartha Bhattacharyya
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Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices.

Quantum Machine Learning With Python

Author : Santanu Pattanayak
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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 Learn Understand Quantum computing and Quantum machine learning Explore varied domains and the scenarios where Quantum machine learning solutions can be applied Develop expertise in algorithm development in varied Quantum computing frameworks Review the major challenges of building large scale Quantum computers and applying its various techniques Who This Book Is For Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning

Quantum Machine Learning

Author : Peter Wittek
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Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

Quantum Machine Learning for Supervised Pattern Recognition

Author : Maria Schuld
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Quantum Machine Learning

Author : Jordi Riu I Vicente
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We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.

Supervised Learning with Quantum Computers

Author : Maria Schuld
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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.

Principles Of Quantum Artificial Intelligence Quantum Problem Solving And Machine Learning Second Edition

Author : Andreas Miroslaus Wichert
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This unique compendium presents an introduction to problem solving, information theory, statistical machine learning, stochastic methods and quantum computation. It indicates how to apply quantum computation to problem solving, machine learning and quantum-like models to decision making — the core disciplines of artificial intelligence.Most of the chapters were rewritten and extensive new materials were updated. New topics include quantum machine learning, quantum-like Bayesian networks and mind in Everett many-worlds.

QUANTUM MECHANICS AND MACHINE LEARNING

Author : GEORGE. CHAPLINE
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Machine Learning Meets Quantum Physics

Author : Kristof T. Schütt
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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.

Quantum Machine Learning in Chemical Space

Author : Felix Andreas Faber
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Blockchain Big Data and Machine Learning

Author : Neeraj Kumar
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Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of blockchain based data analytic environment. Recent research of security based on big data, blockchain and machine learning has been explained through actual work by practitioners and researchers, including their technical evaluation and comparison with existing technologies. The theoretical background and experimental case studies related to real-time environment are covered as well. Aimed at Senior undergraduate students, researchers and professionals in computer science and engineering and electrical engineering, this book: Converges Blockchain, Big Data and Machine learning in one volume. Connects Blockchain technologies with the data centric applications such Big data and E-Health. Easy to understand examples on how to create your own blockchain supported by case studies of blockchain in different industries. Covers big data analytics examples using R. Includes lllustrative examples in python for blockchain creation.

Quantum Computing and Blockchain in Business

Author : Arunkumar Krishnakumar
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Fintech veteran and venture capitalist, Arunkumar Krishnakumar, cuts through the hype to bring us a first-hand look into how quantum computing and Blockchain together could redefine industries and life as we know it. Key Features Take a practical perspective on quantum computing and Blockchain technologies and their impacts on key industries Gain insights from experts who are applying quantum computing or Blockchain in their fields See where quantum computing and Blockchain are heading, and where the two may intersect Book Description Are quantum computing and Blockchain on a collision course or will they be the most important trends of this decade to disrupt industries and life as we know it? Fintech veteran and venture capitalist Arunkumar Krishnakumar cuts through the hype to bring us a first-hand look into how quantum computing and Blockchain together are redefining industries, including fintech, healthcare, and research. Through a series of interviews with domain experts, he also explores these technologies’ potential to transform national and global governance and policies – from how elections are conducted and how smart cities can be designed and optimized for the environment, to what cyberwarfare enabled by quantum cryptography might look like. In doing so, he also highlights challenges that these technologies have to overcome to go mainstream. Quantum Computing and Blockchain in Business explores the potential changes that quantum computing and Blockchain might bring about in the real world. After expanding on the key concepts and techniques, such as applied cryptography, qubits, and digital annealing, that underpin quantum computing and Blockchain, the book dives into how major industries will be impacted by these technologies. Lastly, we consider how the two technologies may come together in a complimentary way. What you will learn Understand the fundamentals of quantum computing and Blockchain Gain insights from the experts who are using quantum computing and Blockchain Discover the implications of these technologies for governance and healthcare Learn how Blockchain and quantum computing may influence logistics and finance Understand how these technologies are impacting research in areas such as chemistry Find out how these technologies may help the environment and influence smart city development Understand the implications for cybersecurity as these technologies evolve Who this book is for This book is for tech enthusiasts – developers, architects, managers, consultants, and venture capitalists – working in or interested in the latest developments in quantum computing and blockchain. While the book introduces key ideas, terms, and techniques used in these technologies, the main goal of this book is to prime readers for the practical adoption and applications of these technologies across varies industries and walks of life.

Quantum Algorithms for Linear Algebra and Machine Learning

Author : Anupam Prakash
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Most quantum algorithms offering speedups over classical algorithms are based on the three techniques of phase estimation, amplitude estimation and Hamiltonian simulation. In spite of the linear algebraic nature of the postulates of quantum mechanics, until recent work by Lloyd and coauthors cite{LMR13, LMR13a, LMR13b} no quantum algorithms achieving speedups for linear algebra or machine learning had been proposed. A quantum machine learning algorithm must address three issues: encoding of classical data into a succinct quantum representation, processing the quantum representation and extraction of classically useful information from the processed quantum state. In this dissertation, we make progress on all three aspects of the quantum machine learning problem and obtain quantum algorithms for low rank approximation and regularized least squares. The oracle $QRAM$, the standard model studied in quantum query complexity, requires time $O(sqrt{n})$ to encode vectors $v in R^{n}$ into quantum states. We propose simple hardware augmentations to the oracle $QRAM$, that enable vectors $v in R^{n}$ to be encoded in time $O(log n)$, with pre-processing. The augmented $QRAM$ incurs minimal hardware overheads, the pre-processing can be parallelized and is a flexible model that allows storage of multiple vectors and matrices. It provides a framework for designing quantum algorithms for linear algebra and machine learning. Using the augmented $QRAM$ for vector state preparation, we present two different algorithms for singular value estimation where given singular vector $ket{v}$ for $A in R^{mtimes n}$, the singular value $sigma_{i}$ is estimated within additive error $epsilon norm{A}_{F}$. The first algorithm requires time $wt{1/epsilon^{3}}$ and uses the approach for simulating $e^{-i rho}$ in cite{LMR13}. However, the analysis cite{LMR13} does not establish the coherence of outputs, we provide a qualitatively different analysis that uses the quantum Zeno effect to establish coherence and reveals the probabilistic nature of the simulation technique. The second algorithm has a running time $wt{1/epsilon}$ and uses Jordan's lemma from linear algebra and the augmented $QRAM$ to implement reflections. We use quantum singular value estimation to obtain algorithms for low rank approximation by column selection, the algorithms are based on importance sampling from the leverage score distribution. We obtain quadratic speedups for a large class of linear algebra algorithms that rely on importance sampling from the leverage score distribution including approximate least squares and $CX$ and $CUR$ decompositions. Classical algorithms for these problems require time $O(mn log n + poly(1/epsilon))$, the quantum algorithms have running time $O(sqrt{m}poly(1/epsilon, k, Delta))$ where $k, Delta$ are the rank and spectral gap. The running time of the quantum $CX$ decomposition algorithm does not depend on $m$, it is polynomial in problem parameters. We also provide quantum algorithms for $ell_{2}$ regularized regression problems, the quantum ridge regression algorithm requires time $wt{1/mu^{2} delta}$ to output a quantum state that is $delta$ close to the solution, where $mu$ is the regularization parameter.

Blockchain Physics Quantum Computing Distributed Ledgers Machine Learning and Other Smart Network Technologies

Author : Melanie Swan
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Quantum information and contemporary smart network domains are so large and complex as to be beyond the reach of current research approaches. Hence, new theories are needed for their understanding and control. Physics is implicated as smart networks are physical systems comprised of particle-many items interacting and reaching criticality and emergence across volumes of macroscopic and microscopic states. Methods are integrated from statistical physics, information theory, and computer science. Statistical neural field theory and the AdS/CFT correspondence are employed to derive a smart network field theory (SNFT) and a smart network quantum field theory (SNQFT) for the orchestration of smart network systems. Specifically, a smart network field theory (conventional or quantum) is a field theory for the organization of particle-many systems from a characterization, control, criticality, and novelty emergence perspective.This book provides insight as to how quantum information science as a paradigm shift in computing may influence other high-impact digital transformation technologies, such as blockchain and machine learning. Smart networks refer to the idea that the internet is no longer simply a communications network, but rather a computing platform. The trajectory is that of communications networks becoming computing networks (with self-executing code), and perhaps ultimately quantum computing networks. Smart network technologies are conceived as autonomous self-operating computing networks. This includes blockchain economies, deep learning neural networks, autonomous supply chains, self-piloting driving fleets, unmanned aerial vehicles, industrial robotics cloudminds, real-time bidding for advertising, high-frequency trading networks, smart city IoT sensors, and the quantum internet.

Advances in Natural Computation

Author : Lipo Wang
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The three volume set LNCS 3610, LNCS 3611, and LNCS 3612 constitutes the refereed proceedings of the First International Conference on Natural Computation, ICNC 2005, held in Changsha, China, in August 2005 jointly with the Second International Conference on Fuzzy Systems and Knowledge Discovery FSKD 2005 (LNAI volumes 3613 and 3614).The program committee selected 313 carefully revised full papers and 189 short papers for presentation in three volumes from 1887 submissions. The first volume includes all the contributions related to learning algorithms and architectures in neural networks, neurodynamics, statistical neural network models and support vector machines, and other topics in neural network models; cognitive science, neuroscience informatics, bioinformatics, and bio-medical engineering, and neural network applications as communications and computer networks, expert system and informatics, and financial engineering. The second volume concentrates on neural network applications such as pattern recognition and diagnostics, robotics and intelligent control, signal processing and multi-media, and other neural network applications; evolutionary learning, artificial immune systems, evolutionary theory, membrane, molecular, DNA computing, and ant colony systems. The third volume deals with evolutionary methodology, quantum computing, swarm intelligence and intelligent agents; natural computation applications as bioinformatics and bio-medical engineering, robotics and intelligent control, and other applications of natural computation; hardware implementations of natural computation, and fuzzy neural systems as well as soft computing.

Surface Chemistry with Machine Learning and Quantum Mechanics

Author : Venkatesh Botu
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Surface chemistry is a phenomenon manifesting itself in several key areas; catalysis, materials fabrication, and emissions mitigation, to name a few. At the present time, atomistic computational driven efforts to study such processes are dominated by models based on quantum mechanics. Their flexibility in studying diverse chemistries, along with the ability to predict accurate thermodynamic and kinetic insights of surface processes, makes them increasingly popular. From ultra-low temperature and pressure to normal operating conditions these methods are now commonly utilized. Nevertheless, the computational burden inherent in the method renders it insufficient to keep up with the current need for quick discovery, i.e. predicting properties of millions of permutations of materials or the meticulous analysis of a chemical reaction on a material. Consequently, a push to go beyond traditional design and characterization practices to explain materials chemistry is becoming necessary. In this thesis, a new framework that combines quantum mechanics with data-driven machine learning methods is put forth. The premise of such an approach is to mine and find patterns within data and in doing so come up with human fathomable relationships, to help accelerate discovery. Here, I focus on model development, which begins by generating data, identifying descriptors for a process, learning from the data and culminating with model validation. This then enables accelerated estimation of thermodynamic and kinetic properties of surface processes. Two detailed examples of this hybrid approach are discussed; (i) a guided and targeted catalyst design framework to identify optimal dopants to enhance thermochemical dissociation of H2O, and (ii) a force predictive framework (commonly known as force field) to rapidly compute forces on atoms, so as to extend dynamic simulations to length and time scales beyond current quantum mechanical methods.

Quantum enhanced Machine Learning in the NISQ Era

Author : Marco Radic
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Quantum Complexity Analysis Using Machine Learning

Author : Kevin Peters
File Size : 80.12 MB
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Intelligent Computing Everywhere

Author : Alfons Schuster
File Size : 89.49 MB
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This book reflects the current perception in various fields that modern computing applications are becoming increasingly challenged in terms of complexity and intelligence. It investigates the relevance and relationship artificial intelligence maintains with "modern strands of computing". These consist of pervasive computing and ambient intelligence, bioinformatics, neuroinformatics, computing and the mind, non-classical computing and novel computing models, as well as DNA computing and quantum computing.

Multi qubit Gates and Quantum enhanced Deliberation for Machine Learning Using a Trapped ion Quantum Processor

Author : Theeraphot Sriarunothai
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