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The correspondence between long-range interacting quantum spin glasses and combinatorial optimization problems underpins the physical motivation for adiabatic quantum computing. On one hand, in disordered (quantum) spin systems, the focus…

Disordered Systems and Neural Networks · Physics 2024-08-05 Tim Bode , Frank K. Wilhelm

There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS…

Probing the lowest energy configuration of a complex system by quantum annealing was recently found to be more effective than its classical, thermal counterpart. Comparing classical and quantum Monte Carlo annealing protocols on the random…

Disordered Systems and Neural Networks · Physics 2009-11-07 Giuseppe E. Santoro , Roman Martonak , Erio Tosatti , Roberto Car

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…

We study the statistical mechanics of classical and quantum systems in non-equilibrium steady states. Emphasis is placed on systems in strong thermal gradients. Various measures and functional forms of observables are presented. The quantum…

Chaotic Dynamics · Physics 2007-05-23 Dimitri Kusnezov , Eric Lutz , Kenichiro Aoki

Quantum computers use quantum resources to carry out computational tasks and may outperform classical computers in solving certain computational problems. Special-purpose quantum computers such as quantum annealers employ quantum adiabatic…

Other Statistics · Statistics 2021-01-19 Xinyu Song , Yazhen Wang , Shang Wu , Donggyu Kim

Non-equilibrium quantum thermodynamics is essential to describe new devices that operate far from the regime where the usual thermodynamical laws are obeyed. When quantum fluctuations dominate, defining and measuring work and heat, two…

The fluctuation theorems, and in particular, the Jarzynski equality, are the most important pillars of modern non-equilibrium statistical mechanics. We extend the quantum Jarzynski equality together with the Two-Time Measurement Formalism…

Statistical Mechanics · Physics 2018-03-01 Anthony Bartolotta , Sebastian Deffner

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to approximately find lowest energy states. Quantum annealers have scaled up in recent years to tackle increasingly larger and more highly connected…

Quantum Physics · Physics 2025-07-04 Humberto Munoz Bauza , Daniel A. Lidar

This research highlights the potential of quantum annealing in tackling large-scale optimization problems within the airline industry,demonstrating its efficiency for certain problem sizes while also acknowledging its current limitations.…

Quantum Physics · Physics 2026-02-16 Kuntal Adak , Sakshi Kaushik , Rahul Rana

Here we first discuss briefly the quantum annealing technique. We then study the quantum annealing of Sherrington-Kirkpatrick spin glass model with the tuning of both transverse and longitudinal fields. Both the fields are time-dependent…

Statistical Mechanics · Physics 2017-08-17 A Rajak , B K Chakrabarti

Following on from our recent work, we investigate a stochastic approach to non-equilibrium quantum spin systems. We show how the method can be applied to a variety of physical observables and for different initial conditions. We provide…

Statistical Mechanics · Physics 2020-01-24 S. De Nicola , B. Doyon , M. J. Bhaseen

We consider a range of unconventional modifications to Quantum Annealing (QA), applied to an artificial trial problem with continuously tunable difficulty. In this problem, inspired by "transverse field chaos" in larger systems, classical…

Quantum Physics · Physics 2021-03-31 Zhijie Tang , Eliot Kapit

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. The idea is tested by the two models, the transverse Ising model and the traveling salesman…

Quantum Physics · Physics 2007-05-23 Tadashi Kadowaki

We develop a quantum algorithm to solve combinatorial optimization problems through quantum simulation of a classical annealing process. Our algorithm combines techniques from quantum walks, quantum phase estimation, and quantum Zeno…

Quantum Physics · Physics 2007-12-07 R. Somma , S. Boixo , H. Barnum

Quantum annealers are commercial devices aiming to solve very hard computational problems named spin glasses. Just like in metallurgic annealing one slowly cools a ferrous metal, quantum annealers seek good solutions by slowly removing the…

Disordered Systems and Neural Networks · Physics 2025-01-22 Massimo Bernaschi , Isidoro González-Adalid Pemartín , Víctor Martín-Mayor , Giorgio Parisi

With the advent of exascale computing, effective load balancing in massively parallel software applications is critically important for leveraging the full potential of high performance computing systems. Load balancing is the distribution…

Quantum Physics · Physics 2025-01-30 Omer Rathore , Alastair Basden , Nicholas Chancellor , Halim Kusumaatmaja

A quantum analogue of the Jarzynski equality is constructed. This equality connects an ensemble average of exponentiated work with the Helmholtz free-energy difference in a nonequilibrium switching process subject to a thermal heat bath. To…

Statistical Mechanics · Physics 2009-10-31 Satoshi Yukawa

Quantum annealing provides a powerful platform for simulating magnetic materials and realizing statistical physics models, presenting a compelling alternative to classical Monte Carlo methods. We demonstrate that quantum annealers can…

Quantum annealing offers a novel approach to finding the optimal solutions for a variety of computational problems, where the quantum annealing controls influence the observed performance and error mechanisms by tuning the underlying…

Quantum Physics · Physics 2021-01-13 Erica Grant , Travis Humble , Benjamin Stump