Quantum Machine Learning

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

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  • Author : Peter Wittek
  • Publisher : Academic Press
  • Pages : 176 pages
  • ISBN : 0128010991
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKQuantum Machine Learning

Quantum Machine Learning

Quantum Machine Learning
  • Author : Peter Wittek
  • Publisher : Academic Press
  • Release : 10 September 2014
GET THIS BOOKQuantum Machine Learning

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

Quantum Machine Learning

Quantum Machine Learning
  • Author : Siddhartha Bhattacharyya,Indrajit Pan,Ashish Mani,Sourav De,Elizabeth Behrman,Susanta Chakraborti
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 08 June 2020
GET THIS BOOKQuantum Machine Learning

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

Supervised Learning with Quantum Computers

Supervised Learning with Quantum Computers
  • Author : Maria Schuld,Francesco Petruccione
  • Publisher : Springer
  • Release : 30 August 2018
GET THIS BOOKSupervised Learning with Quantum Computers

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

Machine Learning Meets Quantum Physics

Machine Learning Meets Quantum Physics
  • Author : Kristof T. Schütt,Stefan Chmiela,O. Anatole von Lilienfeld,Alexandre Tkatchenko,Koji Tsuda,Klaus-Robert Müller
  • Publisher : Springer Nature
  • Release : 03 June 2020
GET THIS BOOKMachine Learning Meets Quantum Physics

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

Compassionate Artificial Intelligence

Compassionate Artificial Intelligence
  • Author : Amit Ray
  • Publisher : Compassionate AI Lab (An Imprint of Inner Light Publishers)
  • Release : 03 October 2018
GET THIS BOOKCompassionate Artificial Intelligence

In this book Dr. Amit Ray describes the principles, algorithms and frameworks for incorporating compassion, kindness and empathy in machine. This is a milestone book on Artificial Intelligence. Compassionate AI address the issues for creating solutions for some of the challenges the humanity is facing today, like the need for compassionate care-giving, helping physically and mentally challenged people, reducing human pain and diseases, stopping nuclear warfare, preventing mass destruction weapons, tackling terrorism and stopping the exploitation of innocent citizens by

Quantum Machine Learning With Python

Quantum Machine Learning With Python
  • Author : Santanu Pattanayak
  • Publisher : Apress
  • Release : 29 March 2021
GET THIS BOOKQuantum 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,

Pro Deep Learning with TensorFlow

Pro Deep Learning with TensorFlow
  • Author : Santanu Pattanayak
  • Publisher : Apress
  • Release : 06 December 2017
GET THIS BOOKPro Deep Learning with TensorFlow

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep

Principles of Quantum Artificial Intelligence

Principles of Quantum Artificial Intelligence
  • Author : Andreas Wichert
  • Publisher : World Scientific
  • Release : 23 October 2013
GET THIS BOOKPrinciples of Quantum Artificial Intelligence

In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation — Quantum Fourier transform and Grover search. Then, we indicate how these two principles can be applied to problem solving and finally present a general model of a quantum computer that is based on production systems. Contents:IntroductionComputationProblem SolvingInformationReversible AlgorithmsProbabilityIntroduction to Quantum PhysicsComputation with QubitsPeriodicitySearchQuantum Problem-SolvingQuantum CognitionRelated Approaches Readership:

Quantum Machine Learning: An Applied Approach

Quantum Machine Learning: An Applied Approach
  • Author : Santanu Ganguly
  • Publisher : Apress
  • Release : 09 July 2021
GET THIS BOOKQuantum 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

Data Science in Chemistry

Data Science in Chemistry
  • Author : Thorsten Gressling
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 23 November 2020
GET THIS BOOKData Science in Chemistry

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing.

ISCS 2014: Interdisciplinary Symposium on Complex Systems

ISCS 2014: Interdisciplinary Symposium on Complex Systems
  • Author : Ali Sanayei,Otto E. Rössler,Ivan Zelinka
  • Publisher : Springer
  • Release : 28 August 2014
GET THIS BOOKISCS 2014: Interdisciplinary Symposium on Complex Systems

The book you hold in your hands is the outcome of the “2014 Interdisciplinary Symposium on Complex Systems” held in the historical city of Florence. The book consists of 37 chapters from 4 areas of Physical Modeling of Complex Systems, Evolutionary Computations, Complex Biological Systems and Complex Networks. All 4 parts contain contributions that give interesting point of view on complexity in different areas in science and technology. The book starts with a comprehensive overview and classification of complexity problems entitled Physics in the

Machine Learning with Python for Everyone

Machine Learning with Python for Everyone
  • Author : Mark Fenner
  • Publisher : Addison-Wesley Professional
  • Release : 30 July 2019
GET THIS BOOKMachine Learning with Python for Everyone

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas

Learn Quantum Computing with Python and Q#

Learn Quantum Computing with Python and Q#
  • Author : Sarah C. Kaiser,Christopher Granade
  • Publisher : Simon and Schuster
  • Release : 29 June 2021
GET THIS BOOKLearn Quantum Computing with Python and Q#

Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll learn QC fundamentals as you apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Learn Quantum Computing with Python and Q# builds your understanding of quantum computers, using Microsoft’s Quantum Development Kit to abstract away the mathematical complexities. You’ll learn QC basics as you create your own quantum simulator in Python, then move on