AI Foundations Of Quantum Machine Learning
AI Foundations Of Quantum Machine Learning

Author: Jon Adams

Publisher: Green Mountain Computing

Published:

Total Pages: 157

ISBN-13:

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Dive into the cutting-edge intersection of quantum computing and machine learning with "AI Foundations of Quantum Machine Learning." This comprehensive guide invites readers into the exciting world where the realms of artificial intelligence (AI) and quantum mechanics merge, setting the stage for a revolution in AI technologies. With the burgeoning interest in quantum computing's vast potential, this book serves as a beacon, illuminating the intricate concepts and groundbreaking promises of quantum machine learning. Contents Quantum Computing: An Introduction - Begin your journey with a primer on quantum computing, understanding the fundamental quantum mechanics that power advanced data processing. Fundamentals of Machine Learning - Lay the groundwork with an overview of machine learning principles, setting the stage for their quantum leap. Quantum Algorithms for Machine Learning - Discover the transformative potential of quantum algorithms, capable of processing large datasets with unprecedented speed and efficiency. Data Encoding in Quantum Systems - Explore the innovative techniques for encoding data into quantum systems, a crucial step for quantum machine learning. Quantum Machine Learning Models - Delve into the heart of quantum machine learning, examining models that harness quantum mechanics to enhance machine learning capabilities. Training Quantum Neural Networks - Unpack the methodologies for training quantum neural networks, a pioneering approach to AI development. Applications of Quantum Machine Learning - Witness the practical implications of quantum machine learning across various fields, from healthcare to environmental science. Challenges and the Future Landscape - Reflect on the hurdles facing quantum machine learning and envision the future of AI shaped by quantum advancements. Introduction "AI Foundations of Quantum Machine Learning" offers a compelling narrative on the symbiosis of quantum computing and machine learning. Through accessible language and vivid examples, it demystifies complex concepts and showcases the transformative power of quantum technologies in AI. Readers are taken on an enlightening journey, from the basic principles of quantum computing to the forefront of quantum machine learning models and their applications. This book is not merely an academic text; it is a roadmap to the future, encouraging readers to envision a world where AI is redefined by quantum phenomena. Ideal for students, academics, and tech enthusiasts alike, this book bridges the gap between theoretical quantum mechanics and practical machine learning applications. Whether you're looking to understand the basics or explore the future of technology, "AI Foundations of Quantum Machine Learning" is an indispensable resource for anyone eager to grasp the next wave of technological innovation.

Supervised Learning with Quantum Computers
Supervised Learning with Quantum Computers

Author: Maria Schuld

Publisher: Springer

Published: 2018-08-30

Total Pages: 293

ISBN-13: 3319964240

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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.

Hands-On Quantum Machine Learning With Python
Hands-On Quantum Machine Learning With Python

Author: Frank Zickert

Publisher: Independently Published

Published: 2021-06-19

Total Pages: 440

ISBN-13:

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You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.

Machine Learning with Quantum Computers
Machine Learning with Quantum Computers

Author: Maria Schuld

Publisher: Springer Nature

Published: 2021-10-17

Total Pages: 321

ISBN-13: 3030830985

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This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

Quantum Machine Learning With Python
Quantum Machine Learning With Python

Author: Santanu Pattanayak

Publisher: Apress

Published: 2021-03-29

Total Pages: 295

ISBN-13: 9781484265215

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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

AI Foundations of Machine Learning
AI Foundations of Machine Learning

Author: Jon Adams

Publisher: Green Mountain Computing

Published:

Total Pages: 117

ISBN-13:

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AI Foundations of Machine Learning Embark on a clarifying expedition through the vibrant world of AI with "AI Foundations of Machine Learning." This comprehensive guide is meticulously crafted for those eager to unravel the complex mechanisms driving artificial intelligence and for pioneers looking to grasp the foundational stones of future technological advancements. From the fundamentals to the futuristic prospects, this book serves as both an educational journey and an initiation into the realm where data, computation, and potential converge. Contents: Understanding Supervised Learning: Begin your journey with an exploration of supervised learning, where machines learn from data with known outcomes, setting the stage for further complexities. The Mechanics of Unsupervised Learning: Delve into the artistry of AI as it uncovers hidden patterns without explicit instructions, highlighting the autonomy of machine learning. Diving into Neural Networks: Uncover the intricacies of neural networks, AI's approximation of the human brain, capable of recognizing speech, images, and nuances in vast datasets. The Decision Tree Paradigm: Discover the decision-making processes of AI through the decision tree paradigm, where data is systematically divided and conquered. Ensemble Methods Combining Strengths: Learn about the power of ensemble methods, which combine multiple models to enhance predictive accuracy and overcome individual weaknesses. Evaluating Model Performance: Understand the critical aspect of evaluating AI model performance, ensuring the integrity and applicability of machine learning applications. Machine Learning in the Real World: Witness the transformative impact of machine learning across various industries, from healthcare to finance, and how it reshapes our interaction with technology. The Future of Machine Learning: Gaze into the future, anticipating the breakthroughs and challenges of machine learning as it becomes an omnipresent force in our lives. This book is your gateway to understanding and participating in the future of AI, equipped with the knowledge to navigate and contribute to the advancements that lie ahead. Whether you are a student, professional, or enthusiast, "AI Foundations of Machine Learning" offers valuable insights into the ever-evolving field of machine learning, encouraging readers to not only understand but also to innovate in the unfolding story of AI.

Machine Learning Meets Quantum Physics
Machine Learning Meets Quantum Physics

Author: Kristof T. Schütt

Publisher: Springer Nature

Published: 2020-06-03

Total Pages: 473

ISBN-13: 3030402452

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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.

Logical Foundations of Artificial Intelligence
Logical Foundations of Artificial Intelligence

Author: Michael R. Genesereth

Publisher: Morgan Kaufmann

Published: 2012-07-05

Total Pages: 427

ISBN-13: 0128015543

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Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic. The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets. This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system. The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction. The third section introduces modal operators that facilitate representing and reasoning about knowledge. This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes. The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture. End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research. Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner. A bibliography and index complete this comprehensive work.

Quantum Machine Learning
Quantum Machine Learning

Author: Peter Wittek

Publisher: Academic Press

Published: 2014-09-10

Total Pages: 176

ISBN-13: 0128010991

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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 Computing: An Applied Approach
Quantum Computing: An Applied Approach

Author: Jack D. Hidary

Publisher: Springer Nature

Published: 2021-09-29

Total Pages: 422

ISBN-13: 3030832740

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This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and corporate technical training. The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition. This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional resources include a table of operators and circuit elements and a companion GitHub site providing code and updates. Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X.