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arXiv:1007.1277 · Physical Review Letters

Quantum Annealing with the Jarzynski Equality

Masayuki Ohzeki

Published Jul 26, 2010• 33 citations
arXivHTMLPDF

Abstract

We show a practical application of the Jarzynski equality in quantum computation. Its implementation may open a way to solve combinatorial optimization problems, minimization of a real single-valued function, cost function, with many arguments. We consider to incorporate the Jarzynski equality into quantum annealing, which is one of the generic algorithms to solve the combinatorial optimization problem. The ordinary quantum annealing suffers from nonadiabatic transitions whose rate is characterized by the minimum energy gap Δmin of the quantum system under consideration. The quantum sweep speed is therefore restricted to be extremely slow for the achievement to obtain a solution without relevant errors. However, in our strategy shown in the present study, we find that such a difficulty would not matter.

DOI
10.1103/physrevlett.105.050401

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We show a practical application of the Jarzynski equality in quantum computation. Its implementation may open a way to solve combinatorial optimization problems, minimization of a real single-valued function, cost function, with many arguments. We consider to incorporate the Jarzynski equality into quantum annealing, which is one of the generic algorithms to solve the combinatorial optimization problem. The ordinary quantum annealing suffers from nonadiabatic transitions whose rate is characterized by the minimum energy gap Δmin of the quantum system under consideration. The quantum sweep speed is therefore restricted to be extremely slow for the achievement to obtain a solution without relevant errors. However, in our strategy shown in the present study, we find that such a difficulty would not matter.
We show a practical application of an well-known nonequilibrium relation, the Jarzynski equality, in quantum computation. Its implementation may open a way to solve combinatorial optimization problems, minimization of a real single-valued function, cost function, with many arguments. It has been disclosed that the ordinary quantum computational algorithm to solve a kind of hard optimization problems, has a bottleneck that its computational time is restricted to be extremely slow without relevant errors. However, by our novel strategy shown in the present study, we might overcome such a difficulty.
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Quantum annealing is a generic algorithm using quantum-mechanical fluctuations to search for the solution of an optimization problem. The present paper first reviews the fundamentals of quantum annealing and then reports on preliminary results for an alternative method. The review part includes the relationship of quantum annealing with classical simulated annealing. We next propose a novel quantum algorithm which might be available for hard optimization problems by using a classical-quantum mapping as well as the Jarzynski equality introduced in nonequilibrium statistical physics.
Quantum annealing is a promising algorithm for solving combinatorial optimization problems. It searches for the ground state of the Ising model, which corresponds to the optimal solution of a given combinatorial optimization problem. The guiding principle of quantum annealing is the adiabatic theorem in quantum mechanics, which guarantees that a system remains in the ground state of its Hamiltonian if the time evolution is sufficiently slow. According to the adiabatic theorem, the annealing time required for quantum annealing to satisfy the adiabaticity scales inversely proportional to the square of the minimum energy gap between the ground state and the first excited state during time evolution. As a result, finding the ground state becomes significantly more difficult when the energy gap is small, creating a major bottleneck in quantum annealing. Expanding the energy gap is one strategy for improving the performance of quantum annealing; however, its implementation in actual hardware remains difficult. This study proposes a method for efficiently solving instances with small energy gaps by introducing additional local terms to the Hamiltonian and exploiting the diabatic transition remaining in the small energy gap. The proposed method achieves an approximate quadratic speedup of the exponential scaling exponent in time to solution compared to the conventional quantum annealing. In addition, we investigate the transferability of the parameters obtained with the proposed method.
This paper presents an adaptive quantum annealing algorithm designed to improve the performance of quantum annealing in solving combinatorial optimization problems. Traditional quantum annealing relies on a fixed annealing schedule, which may not be optimal for all problems. This work introduces a novel approach that utilizes reinforcement learning to dynamically adjust the annealing parameters during the annealing process, based on the observed progress towards a solution. The algorithm learns an optimal annealing schedule by interacting with the quantum annealing system, maximizing the probability of finding a good solution within a given time limit. We demonstrate the theoretical framework and outline the core components of the adaptive algorithm, highlighting its potential advantages over conventional fixed-schedule quantum annealing. The key performance metric is the solution quality (e.g., objective function value) achieved after annealing. The algorithm's effectiveness is evaluated through conceptual analysis and design considerations. This approach aims to bridge the gap between the theoretical potential of quantum annealing and its practical application in solving real-world combinatorial optimization challenges.