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Open AccessJournal ArticleDOI

Quantum annealing of the traveling-salesman problem.

TLDR
The quantum annealing scheme, even with a drastically simple form of kinetic energy, appears definitely superior to the classical one, when tested on a 1002-city instance of the standard TSPLIB.
Abstract
We propose a path-integral Monte Carlo quantum annealing scheme for the symmetric traveling-salesman problem, based on a highly constrained Ising-like representation, and we compare its performance against standard thermal simulated annealing. The Monte Carlo moves implemented are standard, and consist in restructuring a tour by exchanging two links (two-opt moves). The quantum annealing scheme, even with a drastically simple form of kinetic energy, appears definitely superior to the classical one, when tested on a 1002-city instance of the standard TSPLIB.

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Citations
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Journal ArticleDOI

Colloquium: Quantum annealing and analog quantum computation

TL;DR: The recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations, is reviewed in this paper, where the concept is introduced in successive steps through studying the mapping of such computationally hard problems to classical spin-glass problems.
Journal ArticleDOI

Optimization using quantum mechanics: quantum annealing through adiabatic evolution

TL;DR: A review of quantum annealing can be found in this article, where a quantum adiabatic evolution which tracks the ground state of a suitable time-dependent Hamiltonian is slowly switched off.
Journal ArticleDOI

Mathematical foundation of quantum annealing

TL;DR: In this paper, the convergence conditions of quantum annealing to the target optimal state after an infinite-time evolution following the Schrodinger or stochastic (Monte Carlo) dynamics are presented.
Book

Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice

TL;DR: This monograph presents an introductory overview of Adiabatic quantum computation, a survey of basic principles of quantum computation and what is known about the AQC model and the QA algorithm paradigm, and reviews the D-Wave technology stack.
Journal ArticleDOI

Adiabatic quantum dynamics of a random Ising chain across its quantum critical point

TL;DR: In this article, the authors studied the adiabatic quantum dynamics of a random Ising chain across its quantum critical point, where the transverse field term is proportional to a function, which, as in the Kibble-Zurek mechanism, is linearly reduced to zero in time with a rate.
References
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Journal ArticleDOI

Optimization by Simulated Annealing

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
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Metaheuristics in combinatorial optimization: Overview and conceptual comparison

TL;DR: A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other.
Journal ArticleDOI

Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm

TL;DR: It is conjecture that the analogy with thermodynamics can offer a new insight into optimization problems and can suggest efficient algorithms for solving them.
Journal ArticleDOI

Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms

TL;DR: In this paper, the potential energy surface is transformed into a collection of interpenetrating staircases, and the lowest known structures are located for all Lennard-Jones clusters up to 110 atoms, including a number that have never been found before in unbiased searches.
Journal ArticleDOI

Computing with neural circuits: a model

TL;DR: A new conceptual framework and a minimization principle together provide an understanding of computation in model neural circuits that represent an approximation to biological neurons in which a simplified set of important computational properties is retained.
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