This book highlights recent advances in quantum control technologies with regard to hybrid quantum systems. It addresses the following topics: phonon engineering based on phononic crystals, carbon-based nano materials like graphene and nanotubes, Terahertz light technology for single-molecule and quantum dots, nuclear-spin-based metrology for semiconductor quantum systems, quantum anomalous Hall effect in magnetic topological insulators, chiral three-dimensional photonic crystals, and bio-inspired magnonic systems. Each topic, as a component in the framework of hybrid quantum systems, is...
This book highlights recent advances in quantum control technologies with regard to hybrid quantum systems. It addresses the following topics: phon...
This book introduces the fundamentals of the theory of quantum computing, illustrated with code samples written in Q#, a quantum-specific programming language, and its related Quantum Development Kit. Quantum computing (QC) is a multidisciplinary field that sits at the intersection of quantum physics, quantum information theory, computer science and mathematics, and which may revolutionize the world of computing and software engineering.
The book begins by covering historical aspects of quantum theory and quantum computing, as well as offers a gentle,...
This book introduces the fundamentals of the theory of quantum computing, illustrated with code samples written in Q#, a quantum-specific programmi...
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...
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-toleran...
This book presents theoretical methods and experimental results on the study of multipartite quantum correlations in spin-squeezed Bose–Einstein condensates. Nonclassical correlations in many-body systems are particularly interesting for both fundamental research and practical applications. For their investigation, ultracold atomic ensembles offer an ideal platform, due to their high controllability and long coherence times. In particular, we introduce criteria for detecting and characterizing multipartite entanglement, Einstein–Podolsky–Rosen steering, and...
This book presents theoretical methods and experimental results on the study of multipartite quantum correlations in spin-squeezed Bose–Einstein ...
This book highlights recent advances in quantum control technologies with regard to hybrid quantum systems. It addresses the following topics: phonon engineering based on phononic crystals, carbon-based nano materials like graphene and nanotubes, Terahertz light technology for single-molecule and quantum dots, nuclear-spin-based metrology for semiconductor quantum systems, quantum anomalous Hall effect in magnetic topological insulators, chiral three-dimensional photonic crystals, and bio-inspired magnonic systems. Each topic, as a component in the framework of hybrid quantum systems, is...
This book highlights recent advances in quantum control technologies with regard to hybrid quantum systems. It addresses the following topics: phon...
This book covers recent developments in the understanding, quantification, and exploitation of entanglement in spin chain models from both condensed matter and quantum information perspectives. Spin chain models are at the foundation of condensed matter physics and quantum information technologies and elucidate many fundamental phenomena such as information scrambling, quantum phase transitions, and many-body localization. Moreover, many quantum materials and emerging quantum devices are well described by spin chains. Comprising accessible, self-contained chapters written by leading...
This book covers recent developments in the understanding, quantification, and exploitation of entanglement in spin chain models from both condensed m...
This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and...
This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may s...
This book surveys state-of-the-art research on adversarial robustness of quantum machine learning algorithms. Despite their high efficiency and accuracy, classical ML and AI algorithms can be easily fooled by an adversary through manipulation or spoofing of data (also known as adversarial attacks), which poses serious security ramifications. On the other hand, the integration of quantum computing in ML and AI is progressing rapidly to create new quantum ML/AI models which are designed to fundamentally exploit quantum mechanical properties to gain advantages in aspects such as training...
This book surveys state-of-the-art research on adversarial robustness of quantum machine learning algorithms. Despite their high efficiency and acc...
