Optimization algorithms for Quantum Annealing

Arnab Das, Bikas K. Chakrabarti

BaoNan Wang, HengHua SHUI, Sumin Wang +2 more

Quantum annealing (QA) algorithm is a new quantum optimization algorithm based on the classical simulated annealing (SA) algorithm. Unlike classical SA algorithm, which uses the thermal waves to search for the optimal solution, the QA algorithm uses the quantum tunneling effect to allow a quantum to penetrate a potential barrier with a higher energy than itself. Therefore, the algorithm can get rid of the local extreme values and is more likely to approach the global optimal with higher probability. In the literature, the QA algorithm has shown perfect optimization effects on combinatorial optimization problems. In this paper, the basic concepts and application fields of QA algorithm, which is the core principle of D-Wave quantum computer, are systematically reviewed. The applications of QA algorithm in cryptography, traveling salesman problem, graph coloring problem, and traffic path are discussed in detail. In addition, the direction of future research for QA algorithm is explored.

Alfonso de la Fuente Ruiz

Brief description on the state of the art of some local optimization methods: Quantum annealing Quantum annealing (also known as alloy, crystallization or tempering) is analogous to simulated annealing but in substitution of thermal activation by quantum tunneling. The class of algorithmic methods for quantum annealing (dubbed: 'QA'), sometimes referred by the italian school as Quantum Stochastic Optimization ('QSO'), is a promising metaheuristic tool for solving local search problems in multivariable optimization contexts.

Du Weilin, Li Bin, Yu Peng Tian

Shu Tanaka

Evelina Forno, Riccardo Pignari, Vittorio Fra +2 more

Quantum Annealing (QA) is a metaheuristic designed to enhance Simulated Annealing by leveraging concepts from quantum mechanics, improving parallelization on classical computers. Studies have shown promising results for this technique in the field of NP-hard problems and constrained optimization. In this article, we examine Path Integral Quantum Annealing (PIQA), a well-known technique for simulating QA on conventional computers. We then propose optimizations to the algorithm, offering hardware software developers a suite of parallelization techniques evaluated for their effectiveness in enhancing quality and speed. The proposed approach encompasses four distinct degrees of optimization, leveraging techniques based on multiple-trial parallelism and a novel pre-optimization method. The article further proposes a methodology for handling multiple instances within the search space, whereby problem data is replicated into slices and allocated to concurrent processes during the simulation. Through empirical trials, we evaluate the impact of our optimization techniques on the convergence speed of the algorithm compared to unoptimized PIQA, using the Multidimensional Knapsack Problem as a benchmark. Our findings show that these optimizations, applied individually or collectively, enable the algorithm to achieve equal or superior results with fewer simulation steps. Overall, the results highlight the potential for future implementations of optimized PIQA on dedicated hardware.