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arXiv:math/0405572 · Journal of the Korean Statistical Society

Teleportation into Quantum Statistics

Richard David Gill

Published May 29, 2004• 9 citations
arXivHTMLPDF

Abstract

The paper is a tutorial introduction to quantum information theory, developing the basic model and emphasizing the role of statistics and probability.
DOI
10.48550/arxiv.math/0405572

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The Haar measure plays a vital role in quantum information, but its study often requires a deep understanding of representation theory, posing a challenge for beginners. This tutorial aims to provide a basic introduction to Haar measure tools in quantum information, utilizing only basic knowledge of linear algebra and thus aiming to make this topic more accessible. The tutorial begins by introducing the Haar measure with a specific emphasis on characterizing the moment operator, an essential element for computing integrals over the Haar measure. It also covers properties of the symmetric subspace and introduces helpful tools like tensor network diagrammatic notation, which aid in visualizing and simplifying calculations. Next, the tutorial explores the concept of unitary designs, providing equivalent definitions, and subsequently explores approximate notions of unitary designs, shedding light on the relationships between these different notions. Practical examples of Haar measure calculations are illustrated, including the derivation of well-known formulas such as the twirling of a quantum channel. Lastly, the tutorial showcases the applications of Haar measure calculations in quantum machine learning and classical shadow tomography.
Abstract Quantum information science is a new and rapidly developing discipline that studies the general laws governing the transmission, storage, and processing of information in systems that obey the laws of quantum mechanics. It makes extensive use of matrix theory and operator theory on Hilbert spaces to investigate the capabilities of such systems and to develop principles for their rational design. An important consequence of the development of quantum information science is a substantial clarification of the logical structure and foundations of quantum mechanics and of its relation to reality. These lectures are devoted to the foundations of the mathematical theory of quantum systems: states, observables, communication channels, their entropic and information characteristics, and quantum information-processing protocols. The lectures provide a detailed introduction to the statistical structure of quantum theory and the necessary background from classical information theory; hence, knowledge of the basic mathematical subjects—analysis, linear algebra, and probability theory—is sufficient for understanding the material. In the first part of the course, we use the “standard” statistical model of a quantum system, but consider it in comparison with the classical statistical description. A consistent development of this approach leads to a generalized statistical model of quantum mechanics, in which observables (in general, “unsharp”) are described by probability operator-valued measures, while evolutions (in general, irreversible) are described by completely positive maps. Introductory material on these concepts, which underlie mathematical models of quantum information systems in the presence of noise and errors and have numerous applications in quantum information science, is presented in the second part of the course.
Abstract BosonSampling is a recent development in linear optics which has stimulated a number of new computational models and algorithms in quantum computing. Much of this interest follows from the computational complexity arguments which show that the particular quantum system in which BosonSampling resides is difficult to simulate with a classical computer (assuming highly plausible conjectures) Arkhipov and Aaronson (2011 Proc. ACM STOC (New York) , p 333). These arguments, however, require some understanding of both quantum optics and complexity theory—topics that are rarely seen together. This tutorial is intended to introduce a reader to the topic who has at least a basic knowledge of quantum mechanics or quantum information theory, but may be unfamiliar with either quantum optics or complexity theory. Included is some historic context as well as an outlook on the potential for BosonSampling to be the first experimental demonstration of quantum supremacy. The emphasis of the first section is to develop a general context for the subfields of quantum optics and computer science that intersect to form the BosonSampling problem. This begins with a historical introduction and a background of quantum optics. We then progress to the physical system—quantum optical networks—where BosonSampling is defined. Finally, we provide a short introduction to computational complexity theory to provide a structure where the essence behind the BosonSampling result can be discerned. The second section focuses on the BosonSampling result itself, seeking to understand the computational complexity aspects of passive linear optical networks, and what consequences this may have. Some effort is spent discussing a number of issues inherent in the BosonSampling problem that are roadblocks to limiting the scope of its applicability, but that have made progress recently and remain active topics of research. The third short section touches on the relationship between BosonSampling and other quantum computing protocols. Namely, other settings and architectures that inherit the same complexity as BosonSampling will be presented, as well as giving a summary of experimental efforts to implement BosonSampling . We discuss similarities between BosonSampling and other quantum algorithms. In the final section, some comments are presented considering future research related to the problems and protocols found in this tutorial. We suggest other unexplored directions of study which may have some bearing on the complexity of quantum optics. Also included is a glossary of complexity terms used throughout the tutorial.