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Classical and quantum annealing is discussed for a kinetically constrained chain of $N$ non-interacting asymmetric double wells, represented by Ising spins in a longitudinal field $h$. It is shown that in certain cases, where the kinetic…

统计力学 · 物理学 2016-08-31 Arnab Das , Bikas K. Chakrabarti , Robin B. Stinhcombe

Quantum dynamics, typically expressed in the form of a time-dependent Schr\"odinger equation with a Hermitian Hamiltonian, is a natural application for quantum computing. However, when simulating quantum dynamics that involves the emission…

量子物理 · 物理学 2023-04-04 Shi Jin , Nana Liu , Xiantao Li , Yue Yu

Quantum annealing is a contender to solve combinatorial optimization problems based on quantum dynamics. While significant efforts have been undertaken to investigate the quality of the solutions and the required runtimes, much less…

统计力学 · 物理学 2023-06-05 Artem Rakcheev , Andreas M. Läuchli

By analyzing the characteristics of hardware-native Ising Models and their performance on current and next generation quantum annealers, we provide a framework for determining the prospect of advantage utilizing adiabatic evolution compared…

量子物理 · 物理学 2024-06-25 Salvatore Certo , Georgios Korpas , Andrew Vlasic , Philip Intallura

Experiments on disordered alloys suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Due to the importance of spin glasses as a paradigmatic…

Quantum annealing was originally proposed as an approach for solving combinatorial optimisation problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and…

无序系统与神经网络 · 物理学 2021-03-16 Takehito Sato , Masayuki Ohzeki , Kazuyuki Tanaka

We introduce and review briefly the phenomenon of quantum annealing and analog computation. The role of quantum fluctuation (tunneling) in random systems with rugged (free) energy landscapes having macroscopic barriers are discussed to…

统计力学 · 物理学 2023-10-16 Bikas K Chakrabarti , Sudip Mukherjee

Quantum annealing (QA) with a transverse field often fails to sample degenerate ground states fairly, limiting applicability to problems requiring diverse optimal solutions. Although Quantum Monte Carlo (QMC) is widely used to simulate QA,…

量子物理 · 物理学 2025-10-14 Naoki Maruyama , Masayuki Ohzeki , Kazuyuki Tanaka

We show, in the context of quantum combinatorial optimization, or quantum annealing, how the nonlinear Schr\"odinger-Langevin-Kostin equation can dynamically drive the system toward its ground state. We illustrate, moreover, how a…

量子物理 · 物理学 2011-07-05 Diego de Falco , Dario Tamascelli

Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-based solvers require a combination of annealing and classical…

量子物理 · 物理学 2015-07-30 Kenneth M. Zick , Omar Shehab , Matthew French

Reverse annealing is a variant of quantum annealing that starts from a given classical configuration of spins (qubits). In contrast to the conventional formulation, where one starts from a uniform superposition of all possible states…

量子物理 · 物理学 2018-08-21 Masaki Ohkuwa , Hidetoshi Nishimori , Daniel A. Lidar

Geometric frustration in two-dimensional Ising models allows for a wealth of exotic universal behavior, both Ising and non-Ising, in the presence of quantum fluctuations. In particular, the triangular antiferromagnet and Villain model in a…

量子物理 · 物理学 2025-02-13 Ammar Ali , Hanjing Xu , William Bernoudy , Alberto Nocera , Andrew D. King , Arnab Banerjee

In a recent study (Ref. [1]), quantum annealing was reported to exhibit a scaling advantage for approximately solving Quadratic Unconstrained Binary Optimization (QUBO). However, this claim critically depends on the choice of classical…

量子物理 · 物理学 2025-05-29 J. Pawlowski , P. Tarasiuk , J. Tuziemski , L. Pawela , B. Gardas

Quantum annealing aims to solve combinatorial optimization problems mapped on to Ising interactions between quantum spins. A critical factor that limits the success of a quantum annealer is its sensitivity to noise, and intensive research…

量子物理 · 物理学 2019-01-29 Shruti Puri , Christian Kraglund Andersen , Arne L. Grimsmo , Alexandre Blais

We propose that the importance of the quantum annealing procedure to find the ground state of frustrated decorated bond systems where 'entropic slowing down' happens due to peculiar density of states. Here, we use the time dependent…

无序系统与神经网络 · 物理学 2007-05-23 Shu Tanaka , Seiji Miyashita

We use quantum Monte Carlo methods and various analytic approximations to solve the Ising spin-glass model in a transverse field in the disordered phase. We focus on the behavior of the frequency dependent susceptibility of the system above…

无序系统与神经网络 · 物理学 2009-10-31 M. J. Rozenberg , D. R. Grempel

Quantum annealing (QA) is a method for solving combinatorial optimization problems. We can estimate the computational time for QA using the adiabatic condition. The adiabatic condition consists of two parts: an energy gap and a transition…

量子物理 · 物理学 2024-08-28 Hiroshi Hayasaka , Takashi Imoto , Yuichiro Matsuzaki , Shiro Kawabata

Quantum Ising model is an exactly solvable model of quantum phase transition. This paper gives an exact solution when the system is driven through the critical point at finite rate. The evolution goes through a series of Landau-Zener level…

其他凝聚态物理 · 物理学 2009-11-11 Jacek Dziarmaga

We propose a nonadiabatic approach to quantum annealing, in which we repeat quantum annealing in nonadiabatic time scales, and collect the final states of many realizations to find the ground state among them. In this way, we replace the…

统计力学 · 物理学 2013-03-26 Hitoshi Katsuda , Hidetoshi Nishimori

Annealing schedule control provides new opportunities to better understand the manner and mechanisms by which putative quantum annealers operate. By appropriately modifying the annealing schedule to include a pause (keeping the Hamiltonian…

量子物理 · 物理学 2021-01-22 Tameem Albash , Jeffrey Marshall