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We explore the role of entanglement in adiabatic quantum optimization by performing approximate simulations of the real-time evolution of a quantum system while limiting the amount of entanglement. To classically simulate the time evolution…

Disordered Systems and Neural Networks · Physics 2015-01-29 Bela Bauer , Lei Wang , Iztok Pižorn , Matthias Troyer

A quantum mechanical explanation of the relaxation to equilibrium is shown for macroscopic systems for nonintegrable cases and numerically verified. The macroscopic system is initially in an equilibrium state, subsequently externally…

Statistical Mechanics · Physics 2015-05-28 Takaaki Monnai

Quantum annealing is a computational paradigm in which optimisation problems are mapped onto the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming a simple initial…

Quantum Physics · Physics 2026-05-11 Steven Abel , Andrei Constantin , Luca A. Nutricati

We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate generalizability to…

Quantum Physics · Physics 2017-04-27 E. C. Behrman , J. E. Steck , M. A. Moustafa

This paper explores the phenomenon of avoided level crossings in quantum annealing, a promising framework for quantum computing that may provide a quantum advantage for certain tasks. Quantum annealing involves letting a quantum system…

Quantum Physics · Physics 2024-04-12 Arthur Braida , Simon Martiel , Ioan Todinca

Providing an optimal path to a quantum annealing algorithm is key to finding good approximate solutions to computationally hard optimization problems. Reinforcement is one of the strategies that can be used to circumvent the exponentially…

Disordered Systems and Neural Networks · Physics 2022-07-27 Abolfazl Ramezanpour

Quantum annealing can efficiently obtain solutions to combinatorial optimization problems. Size-reduction methods are used to treat large-scale combinatorial optimization problems that cannot be input directly into a quantum annealer…

Statistical Mechanics · Physics 2024-12-30 Tomohiro Hattori , Hirotaka Irie , Tadashi Kadowaki , Shu Tanaka

To solve an optimization problem using a commercial quantum annealer, one has to represent the problem of interest as an Ising or a quadratic unconstrained binary optimization (QUBO) problem and submit its coefficients to the annealer,…

Quantum Physics · Physics 2023-06-13 Elijah Pelofske , Georg Hahn , Hristo Djidjev

We propose a modified quantum annealing protocol, i. e., pulsed quantum annealing} (PQA), in order to increase the success probability by a pulse application during the quantum annealing process. It is well known that the success…

Quantum Physics · Physics 2020-08-18 Vasilios Karanikolas , Shiro Kawabata

A mechanism of the parity effect in the thermally assisted resonant tunneling is proposed in the view point of nonadiabatic transitions of thermally excited states. In this mechanism, alternating enhancement of the relaxation is naturally…

Statistical Mechanics · Physics 2007-05-23 Seiji Miyashita , Keiji Saito , Hiroto Kobayashi

Simultaneously implementing two arbitrary quantum measurements on the same system is impossible. The consequence of this limitation is that selecting one measurement actively excludes other possibilities. Two incompatible choices can then…

Quantum Physics · Physics 2026-02-13 C. Carmeli , T. Heinosaari , A. Toigo

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…

Quantum Physics · Physics 2024-08-28 Hiroshi Hayasaka , Takashi Imoto , Yuichiro Matsuzaki , Shiro Kawabata

It was recently shown that certain subsurface hydrological inverse problems -- here framed as determining the composition of an aquifer from pressure readings -- can be solved on a quantum annealer. However, the quantum annealer performance…

Emerging Technologies · Computer Science 2020-04-21 John K. Golden , Daniel O'Malley

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…

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

Diabatic quantum annealing aims to mitigate the challenges posed by small energy gaps and decoherence in quantum optimization by exploiting nonadiabatic transitions. In this paper, we compare the performance of two diabatic protocols in a…

Quantum annealing is an optimization technique which potentially leverages quantum tunneling to enhance computational performance. Existing quantum annealers use superconducting flux qubits with short coherence times, limited primarily by…

Quantum state discrimination, alongside its other applications, has recently found use as a tool for witnessing generalised contextuality. In this article, we derive noncontextuality inequalities for both conclusive and inconclusive…

Quantum Physics · Physics 2025-11-07 Kieran Flatt , Joonwoo Bae

Finding quantitative aspects of quantum phenomena which cannot be explained by any classical model has foundational importance for understanding the boundary between classical and quantum theory. It also has practical significance for…

Quantum Physics · Physics 2018-02-06 David Schmid , Robert W. Spekkens

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

The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations…

Optimization and Control · Mathematics 2020-10-21 Yuchen Pang , Carleton Coffrin , Andrey Y. Lokhov , Marc Vuffray