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Quantum Technology and Optimization Problems by Sebastian Feld,Claudia Linnhoff-Popien Pdf
This book constitutes the refereed proceedings of the First International Workshop on Quantum Technology and Optimization Problems, QTOP 2019, held in Munich, Germany, in March 2019.The 18 full papers presented together with 1 keynote paper in this volume were carefully reviewed and selected from 21 submissions. The papers are grouped in the following topical sections: analysis of optimization problems; quantum gate algorithms; applications of quantum annealing; and foundations and quantum technologies.
A Practical Guide to Quantum Machine Learning and Quantum Optimization by Elias F. Combarro,Samuel Gonzalez-Castillo,Alberto Di Meglio Pdf
Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide Key FeaturesGet a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisitesLearn the process of implementing the algorithms on simulators and actual quantum computersSolve real-world problems using practical examples of methodsBook Description This book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You'll be introduced to quantum computing using a hands-on approach with minimal prerequisites. You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that's ready to be run on quantum simulators and actual quantum computers. You'll also learn how to utilize programming frameworks such as IBM's Qiskit, Xanadu's PennyLane, and D-Wave's Leap. Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away. What you will learnReview the basics of quantum computingGain a solid understanding of modern quantum algorithmsUnderstand how to formulate optimization problems with QUBOSolve optimization problems with quantum annealing, QAOA, GAS, and VQEFind out how to create quantum machine learning modelsExplore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLaneDiscover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interfaceWho this book is for This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
Research Anthology on Advancements in Quantum Technology by Management Association, Information Resources Pdf
Quantum technology has arrived as one of the most important new topics of research, as it is the newest way to create computing power, harness secure communications, and use sensitive measurement methods that surpass the capabilities of modern supercomputers. If successfully developed, quantum computers and technology will be able to perform algorithms at impressively quick rates and solve problems that were previously deemed impossible. This technology will disrupt what is already known about computing and will be able to reach new heights, speeds, and problem-solving capabilities not yet seen. Beyond its inherent benefits comes the fact that quantum technology will create improvements in many everyday gadgets as well, spanning many industries. The Research Anthology on Advancements in Quantum Technology presents the latest discoveries in quantum technology itself along with providing its essential uses, applications, and technologies that will impact computing in modern times and far into the future. Along with this overview comes a look at quantum technology in many different fields such as healthcare, communications, aviation, automotive, forecasting, and more. These industries will be looked at from the perspective of data analytics, pattern matching, cryptography, algorithms, and more. This book is essential for computer scientists, engineers, professionals, researchers, students, and practitioners interested in the latest information on quantum technology.
A Practical Guide to Quantum Machine Learning and Quantum Optimisation by Elias F. Combarro,Samuel Gonzalez-Castillo Pdf
Work with fully explained algorithms and ready-to-use examples that can be run on quantum simulators and actual quantum computers with this comprehensive guide Key Features: Get a solid grasp of the principles behind quantum algorithms and optimization with minimal mathematical prerequisites Learn the process of implementing the algorithms on simulators and actual quantum computers Solve real-world problems using practical examples of methods Book Description: This book provides deep coverage of modern quantum algorithms that can be used to solve real-world problems. You'll be introduced to quantum computing using a hands-on approach with minimal prerequisites. You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms, and you will find out how to solve optimization problems with quantum annealing, QAOA, Grover Adaptive Search (GAS), and VQE. This book also shows you how to train quantum machine learning models, such as quantum support vector machines, quantum neural networks, and quantum generative adversarial networks. The book takes a straightforward path to help you learn about quantum algorithms, illustrating them with code that's ready to be run on quantum simulators and actual quantum computers. You'll also learn how to utilize programming frameworks such as IBM's Qiskit, Xanadu's PennyLane, and D-Wave's Leap. Through reading this book, you will not only build a solid foundation of the fundamentals of quantum computing, but you will also become familiar with a wide variety of modern quantum algorithms. Moreover, this book will give you the programming skills that will enable you to start applying quantum methods to solve practical problems right away. What You Will Learn: Review the basics of quantum computing Gain a solid understanding of modern quantum algorithms Understand how to formulate optimization problems with QUBO Solve optimization problems with quantum annealing, QAOA, GAS, and VQE Find out how to create quantum machine learning models Explore how quantum support vector machines and quantum neural networks work using Qiskit and PennyLane Discover how to implement hybrid architectures using Qiskit and PennyLane and its PyTorch interface Who this book is for: This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
🌟 Explore the Future with the "Quantum Computing: Computer Science, Physics, and Mathematics" Book Bundle! 🌟 Are you ready to unlock the secrets of quantum computing and delve into the multidisciplinary world of computer science, physics, and mathematics? Look no further! Our exclusive bundle, consisting of four captivating books, is your ticket to the quantum frontier. 📚 Book 1: "Quantum Computing Demystified: A Beginner's Guide" 📚 Are you new to quantum computing? This beginner's guide will unravel the complex concepts and lay the foundation for your quantum journey. Dive into qubits, superposition, and quantum algorithms, and embark on a transformative exploration of quantum computing's limitless possibilities. 📚 Book 2: "Mastering Quantum Computing: A Comprehensive Guide for Intermediate Learners" 📚 Ready to take the next step? This comprehensive guide is tailored for intermediate learners, providing in-depth insights into advanced topics, quantum programming, and algorithm design. Elevate your skills and become a quantum computing virtuoso. 📚 Book 3: "Advanced Quantum Computing: Exploring the Frontiers of Computer Science, Physics, and Mathematics" 📚 Step into the cutting-edge world of quantum computing's frontiers. Delve into quantum error correction, cryptography, and simulations, and discover the complex challenges and captivating possibilities that await at the forefront of this transformative technology. 📚 Book 4: "Quantum Computing: A Multidisciplinary Approach for Experts" 📚 Quantum computing transcends disciplines, and this book proves it. Explore its multifaceted applications in computer science, physics, mathematics, and beyond. Recognize its potential to reshape industries and address global challenges. This book is a must-read for experts and visionaries. ✨ Why Choose This Bundle? ✨ 🚀 Comprehensive Learning: Our bundle offers a 360-degree view of quantum computing, catering to beginners and experts alike. 🎓 Multidisciplinary Insights: Explore the intersections of computer science, physics, mathematics, and quantum computing for innovative perspectives. 💡 Future-Ready: Quantum computing is at the forefront of technology. Equip yourself with the skills and knowledge that will shape the future. 📖 In-Depth Exploration: Dive deep into quantum mechanics, algorithms, error correction, and applications, unraveling the complexities along the way. 🌌 The quantum frontier awaits your exploration. This bundle is your key to unlocking the boundless potential of quantum computing while understanding its multidisciplinary impact. 🌟 Don't miss this opportunity to embark on a transformative journey into the future of technology! 🌟 Secure your "Quantum Computing: Computer Science, Physics, and Mathematics" book bundle today and prepare to be amazed by the endless possibilities of quantum computing. Elevate your skills, expand your knowledge, and become a quantum trailblazer! 🚀 Grab this bundle now and step into the quantum realm, where the future of computing, science, and mathematics converges! 🚀
Frontiers of Engineering by National Academy of Engineering Pdf
This volume presents papers on the topics covered at the National Academy of Engineering's 2018 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2018 symposium was held September 5-7 and hosted by MIT Lincoln Laboratory in Lexington, Massachusetts. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work.
Approximability of Optimization Problems through Adiabatic Quantum Computation by William Cruz-Santos,Guillermo Morales-Luna Pdf
The adiabatic quantum computation (AQC) is based on the adiabatic theorem to approximate solutions of the Schrödinger equation. The design of an AQC algorithm involves the construction of a Hamiltonian that describes the behavior of the quantum system. This Hamiltonian is expressed as a linear interpolation of an initial Hamiltonian whose ground state is easy to compute, and a final Hamiltonian whose ground state corresponds to the solution of a given combinatorial optimization problem. The adiabatic theorem asserts that if the time evolution of a quantum system described by a Hamiltonian is large enough, then the system remains close to its ground state. An AQC algorithm uses the adiabatic theorem to approximate the ground state of the final Hamiltonian that corresponds to the solution of the given optimization problem. In this book, we investigate the computational simulation of AQC algorithms applied to the MAX-SAT problem. A symbolic analysis of the AQC solution is given in order to understand the involved computational complexity of AQC algorithms. This approach can be extended to other combinatorial optimization problems and can be used for the classical simulation of an AQC algorithm where a Hamiltonian problem is constructed. This construction requires the computation of a sparse matrix of dimension 2n × 2n, by means of tensor products, where n is the dimension of the quantum system. Also, a general scheme to design AQC algorithms is proposed, based on a natural correspondence between optimization Boolean variables and quantum bits. Combinatorial graph problems are in correspondence with pseudo-Boolean maps that are reduced in polynomial time to quadratic maps. Finally, the relation among NP-hard problems is investigated, as well as its logical representability, and is applied to the design of AQC algorithms. It is shown that every monadic second-order logic (MSOL) expression has associated pseudo-Boolean maps that can be obtained by expanding the given expression, and also can be reduced to quadratic forms. Table of Contents: Preface / Acknowledgments / Introduction / Approximability of NP-hard Problems / Adiabatic Quantum Computing / Efficient Hamiltonian Construction / AQC for Pseudo-Boolean Optimization / A General Strategy to Solve NP-Hard Problems / Conclusions / Bibliography / Authors' Biographies
Quantum Computing Solutions by Bhagvan Kommadi Pdf
Know how to use quantum computing solutions involving artificial intelligence (AI) algorithms and applications across different disciplines. Quantum solutions involve building quantum algorithms that improve computational tasks within quantum computing, AI, data science, and machine learning. As opposed to quantum computer innovation, quantum solutions offer automation, cost reduction, and other efficiencies to the problems they tackle. Starting with the basics, this book covers subsystems and properties as well as the information processing network before covering quantum simulators. Solutions such as the Traveling Salesman Problem, quantum cryptography, scheduling, and cybersecurity are discussed in step-by-step detail. The book presents code samples based on real-life problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and protein-folding solutions. Supply chain optimization and purchasing solutions are presented in the manufacturing domain. In the area of utilities, energy distribution and optimization problems and solutions are explained. Advertising scheduling and revenue optimization solutions are included from media and technology verticals. You will: Understand the mathematics behind quantum computing Know the solution benefits, such as automation, cost reduction, and efficiencies Be familiar with the quantum subsystems and properties, including states, protocols, operations, and transformations Be aware of the quantum classification algorithms: classifiers, and support and sparse support vector machines Use AI algorithms, including probability, walks, search, deep learning, and parallelism.
Quantum Computing Experimentation with Amazon Braket by Alex Khan Pdf
Augment your quantum computing journey using Amazon Braket with in-depth details of how to use Braket devices, build quantum circuits, leverage quantum annealing and solve optimization problems Key Features Learn about various quantum devices and their use in solving combinatorial optimization problems Prepare your business to leverage the benefits of the future quantum speedup Implement quantum computing concepts through real-world optimization use cases Book DescriptionAmazon Braket is a cloud-based pay-per-use platform for executing quantum algorithms on cutting-edge quantum computers and simulators. It is ideal for developing robust apps with the latest quantum devices. With this book, you'll take a hands-on approach to learning how to take real-world problems and run them on quantum devices. You'll begin with an introduction to the Amazon Braket platform and learn about the devices currently available on the platform, their benefits, and their purpose. Then, you'll review key quantum concepts and algorithms critical to converting real-world problems into a quantum circuit or binary quadratic model based on the appropriate device and its capability. The book also covers various optimization use cases, along with an explanation of the code. Finally, you'll work with a framework using code examples that will help to solve your use cases with quantum and quantum-inspired technologies. Later chapters cover custom-built functions and include almost 200 figures and diagrams to visualize key concepts. You’ll be able to scan the capabilities provided by Amazon Braket and explore the functions to adapt them for specific use cases. By the end of this book, you’ll have the tools to integrate your current business apps and AWS data with Amazon Braket to solve constrained and multi-objective optimization problems.What you will learn Explore the features and uses of the Amazon Braket console and components Discover the benefits of quantum computing devices available on Amazon Braket, including gate quantum computers, the annealer, and simulators Recognize which type of quantum device is the best fit for specific use cases and scaling Develop your own code from a basic set of use cases dealing with real-world optimization problems Understand the capabilities and limitations of current quantum computing technologies Explore the advanced features and API functions in Amazon Braket Who this book is for This book is for IT practitioners, architects, and developers looking to bring the power of quantum computing to their organizations. If you are a VP of IT, CIO, VP of architecture, chief architect, solution architect, actuarial fellow, or a developer already working on other AWS services such as AWS Lambda and EC2, you'll find this book useful in exploring how to leverage Amazon Braket for real-world use cases and to move your organization towards this emerging technology. Familiarity with the basics of quantum computing and Python is required.
Quantum Machine Learning and Optimisation in Finance by Antoine Jacquier,Oleksiy Kondratyev,Alexander Lipton,Marcos Lopez de Prado Pdf
Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key Features Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods Use methods of analogue and digital quantum computing to build powerful generative models Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers Book Description With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learn Train parameterised quantum circuits as generative models that excel on NISQ hardware Solve hard optimisation problems Apply quantum boosting to financial applications Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work Analyse the latest algorithms from quantum kernels to quantum semidefinite programming Apply quantum neural networks to credit approvals Who this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.
"Quantum Computing" is a comprehensive and accessible exploration of one of the most exciting and rapidly evolving fields in modern science. Written with both beginners and advanced enthusiasts in mind, this book offers a captivating journey through the world of quantum computing without the need for complex mathematical formulas. With 50 engaging chapters covering a wide range of topics, readers will discover the fascinating principles behind quantum mechanics and how they are harnessed to revolutionize computing, cryptography, telecommunications, and even our understanding of consciousness and the universe itself. From the basics of qubits and superposition to advanced applications like quantum cryptography and artificial intelligence, this book presents complex concepts in a clear and easy-to-understand manner, making it suitable for readers of all backgrounds. Whether you're a curious novice or a seasoned quantum enthusiast, "Quantum Computing" offers a captivating glimpse into the extraordinary possibilities of the quantum world.
Chapter 1: Introduction to Quantum Machine Learning Overview of Quantum Computing The field of quantum computing is a rapidly evolving area of study that has the potential to revolutionize the world of artificial intelligence (AI) and machine learning. Quantum computing harnesses the principles of quantum mechanics to perform computations that would be impossible for classical computers. By exploiting the unique properties of quantum bits, or qubits, quantum computers have the ability to solve complex problems at an exponentially faster rate than traditional computers. Quantum machine learning is a subset of quantum computing that seeks to leverage the power of quantum algorithms to enhance the capabilities of machine learning models. Quantum neural networks, quantum optimization, and quantum-enhanced feature spaces are just a few examples of how quantum computing can be used to improve the performance of AI systems. These quantum algorithms for AI are designed to tackle problems that are currently beyond the reach of classical machine learning techniques. One of the key advantages of quantum computing for AI is the ability to explore vast solution spaces in a fraction of the time it would take a classical computer. Quantum algorithms can efficiently search for optimal solutions to complex optimization problems, making them ideal for tasks such as drug discovery and materials science. By combining classical and quantum computing in hybrid systems, researchers can leverage the strengths of both approaches to create more powerful AI models. However, as with any emerging technology, there are ethical and societal implications to consider when it comes to quantum computing for AI. Issues such as data privacy, algorithmic bias, and the potential for misuse of quantum technology must be carefully monitored and addressed. It is important for researchers and practitioners in the field of quantum machine learning to actively engage with these ethical considerations to ensure that the benefits of quantum computing are realized in a responsible and sustainable manner. In conclusion, quantum computing holds great promise for the future of AI and machine learning. By unlocking the power of quantum algorithms and quantum-enhanced feature spaces, researchers can push the boundaries of what is possible in the field of artificial intelligence. With careful attention to ethical and societal implications, quantum machine learning has the potential to revolutionize industries such as drug discovery, materials science, and beyond.
