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FROM VIEWPOINTS OF STATISTICAL PHYSICS, CONDENSED MATTER PHYSICS, AND COMPUTATIONAL PHYSICS

Shu Tanaka

Published Aug 16, 2016• 0 citations
OpenAlex

Abstract

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

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In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.
In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.
Quantum annealing leverages the properties of interacting quantum spin systems to solve computational problems, typically optimisation problems. Current hardware now has capabilities that can be used to solve condensed matter physics problems, too. In this topical review, we provide an overview of quantum annealing aimed at condensed matter physicists, to show the mutual benefits of working together to understand and improve how quantum annealers work, and to use them to advance condensed matter physics.
Quantum annealing (QA) is a promising calculation method for obtaining a better solution to combinatorial optimization problems. QA is a natural computation technique proposed in the context of statistical physics in 1998. In 2011, the world's first commercial machine realizing QA experimentally was released. After that, the development of hardware and software has been exhaustive, and the studies on application search have been done using commercial QA machines. In this paper, we first explain the basics of QA without assuming the knowledge of statistical physics and quantum physics. We also show a couple of examples of application studies on QA. In addition, existing problems and overcoming other methods are explained.
Quantum annealing is a generic algorithm using quantum-mechanical fluctuations to search for the solution of an optimization problem. The present paper first reviews the fundamentals of quantum annealing and then reports on preliminary results for an alternative method. The review part includes the relationship of quantum annealing with classical simulated annealing. We next propose a novel quantum algorithm which might be available for hard optimization problems by using a classical-quantum mapping as well as the Jarzynski equality introduced in nonequilibrium statistical physics.
The quantum annealing technique is such a paradigm that is next generation that can be used in solving optimization problem across different scientific domains like finance and material science and is capable of breaking through past obstacles to generate solutions for problems waiting to be solved. The purpose of this paper is the detailed depiction of the application of the quantum annealing in the optimization that includes the principles, applications, methodologies and outcomes and future trends all of them. Through a multidisciplinary approach, we examine the use of quantum annealing, as the method that exploits the specific features of the quantum annealing through the quantum mechanics, to explore the multitude of solution spaces faster and better than the classical methods, leading to the superior solution quality and the faster computing times. Next, we give examples for its implementation in Portfolio optimization, Machine learning, chemical optimization, Traffic management and so on depicting its role in delivering tangible benefits such as higher financial returns, precision improvement, and time savings contrast to the classical methods. Therefore, we also emphasize the current impediments, such as the scaling down the size and the ethical aspect, through which we give the way forward for the subsequent scientific researches and studies. In this abstract, we aim to depict the helix-like nature of quantum annealing in the revolution of optimization, bringing in innovations, and thus, shaping the future in which it is the quantum computing technologies that hold the central stage in addressing the complex interconnecting real-world issues.
Abstract The quantum annealing framework provides a general technique for the solution of a wide variety of optimization problems. Recent technological developments have made it possible to build quantum annealing devices with thousands of qubits, bringing them within range to tackle real-world problems in the near future. In this paper, I review this framework, and present a method for its use in the minimization of functions of continuous variables. Then, I discuss how this method can be employed to solve optimization problems with applications in high-energy physics, including training neural networks for classification, solving partial differential equations, and fitting effective field theories to experimental data.