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We perform an in-depth comparison of quantum annealing with several classical optimisation techniques, namely thermal annealing, Nelder-Mead, and gradient descent. We begin with a direct study of the 2D Ising model on a quantum annealer,…

Quantum Physics · Physics 2022-10-19 Steve Abel , Andrew Blance , Michael Spannowsky

With the advent of quantum computers, researchers are exploring if quantum mechanics can be leveraged to solve important problems in ways that may provide advantages not possible with conventional or classical methods. A previous work by…

Quantum Physics · Physics 2018-11-02 Ajinkya Borle , Samuel J. Lomonaco

Relations of simulated annealing and quantum annealing are studied by a mapping from the transition matrix of classical Markovian dynamics of the Ising model to a quantum Hamiltonian and vice versa. It is shown that these two operators, the…

Quantum Physics · Physics 2015-01-08 Hidetoshi Nishimori , Junichi Tsuda , Sergey Knysh

Brief description on the state of the art of some local optimization methods: Quantum annealing Quantum annealing (also known as alloy, crystallization or tempering) is analogous to simulated annealing but in substitution of thermal…

Data Structures and Algorithms · Computer Science 2014-04-10 Alfonso de la Fuente Ruiz

Quantum annealing is a general strategy for solving difficult optimization problems with the aid of quantum adiabatic evolution. Both analytical and numerical evidence suggests that under idealized, closed system conditions, quantum…

In this paper we present a novel strategy to solve optimization problems within a hybrid quantum-classical scheme based on quantum annealing, with a particular focus on QUBO problems. The proposed algorithm is based on an iterative…

Quantum Physics · Physics 2020-04-07 Enrico Blanzieri , Davide Pastorello

Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale…

Quantum Physics · Physics 2022-10-05 Sheir Yarkoni , Elena Raponi , Thomas Bäck , Sebastian Schmitt

Quantum annealing is an innovative idea and method for avoiding the increase of the calculation cost of the combinatorial optimization problem. Since the combinatorial optimization problems are ubiquitous, quantum annealing machine with…

Statistical Mechanics · Physics 2020-01-13 Shohei Watabe , Yuya Seki , Shiro Kawabata

Quantum tunneling is a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself. Tunneling has been hypothesized as an advantageous physical resource for optimization. Here we present the first…

Tunneling is often claimed to be the key mechanism underlying possible speedups in quantum optimization via quantum annealing (QA), especially for problems featuring a cost function with tall and thin barriers. We present and analyze…

Quantum Physics · Physics 2016-07-26 Siddharth Muthukrishnan , Tameem Albash , Daniel A. Lidar

With progress in quantum technology more sophisticated quantum annealing devices are becoming available. While they offer new possibilities for solving optimization problems, their true potential is still an open question. As the optimal…

Quantum Physics · Physics 2017-02-22 Bettina Heim , Ethan W. Brown , Dave Wecker , Matthias Troyer

Recent advancements in quantum computing suggest the potential to revolutionize computational algorithms across various scientific domains including oceanography and atmospheric science. The field is still relatively young and quantum…

Quantum Physics · Physics 2026-03-24 Takuro Matsuta , Ryo Furue

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to…

We prove several theorems to give sufficient conditions for convergence of quantum annealing, which is a protocol to solve generic optimization problems by quantum dynamics. In particular the property of strong ergodicity is proved for the…

Quantum Physics · Physics 2007-05-23 Satoshi Morita , Hidetoshi Nishimori

Recent advances in quantum technology have led to the development and manufacturing of experimental programmable quantum annealers that promise to solve certain combinatorial optimization problems of practical relevance faster than their…

Quantum Physics · Physics 2016-05-31 Itay Hen , Federico M. Spedalieri

We describe a quantum algorithm that solves combinatorial optimization problems by quantum simulation of a classical simulated annealing process. Our algorithm exploits quantum walks and the quantum Zeno effect induced by evolution…

Quantum Physics · Physics 2009-02-02 R. D. Somma , S. Boixo , H. Barnum , E. Knill

Quantum annealing method has been widely attracted attention in statistical physics and information science since it is expected to be a powerful method to obtain the best solution of optimization problem as well as simulated annealing. The…

Disordered Systems and Neural Networks · Physics 2017-08-23 Shu Tanaka , Ryo Tamura

This paper investigates the basic behavior and performance of simulated quantum annealing (QA) in comparison with classical annealing (CA). Three simple one dimensional case study systems are considered, namely a parabolic well, a double…

Other Condensed Matter · Physics 2009-11-11 Lorenzo Stella , Giuseppe E. Santoro , Erio Tosatti

We present a quantum annealing-based solution method for topology optimization (TO). In particular, we consider TO in a more general setting, i.e., applied to structures of continuum domains where designs are represented as distributed…

Numerical Analysis · Mathematics 2023-01-30 Zisheng Ye , Xiaoping Qian , Wenxiao Pan

We study quantum annealing in the quantum Ising model coupled to a thermal environment. When the speed of quantum annealing is sufficiently slow, the system evolves following the instantaneous thermal equilibrium. This quasistatic and…

Quantum Physics · Physics 2022-05-09 Hiroki Oshiyama , Sei Suzuki , Naokazu Shibata