Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning

TL;DR

This paper investigates the use of metaheuristic algorithms like Particle Swarm Optimization and Simulated Annealing to optimize quantum reinforcement learning circuits, showing promising results in simulated environments.

Contribution

It introduces the integration of various metaheuristic strategies into QRL to address flat solution landscapes and improve parameter optimization.

Findings

Particle Swarm Optimization and Simulated Annealing perform best in MiniGrid environments.

All algorithms achieve near-optimal results, with some reaching optimal solutions in Cart Pole.

Metaheuristics enhance QRL efficiency, highlighting the importance of algorithm choice.

Abstract

Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further validation. QRL faces challenges like flat solution landscapes, where traditional gradient-based methods are inefficient, necessitating the use of gradient-free algorithms. This work explores the integration of metaheuristic algorithms -- Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Genetic Algorithm, Simulated Annealing, and Harmony Search -- into QRL. These algorithms provide flexibility and efficiency in parameter optimization. Evaluations in $5\times5$ MiniGrid Reinforcement Learning environments show that, all algorithms yield near-optimal results, with Simulated Annealing and Particle Swarm Optimization performing best. In the Cart Pole environment, Simulated Annealing, Genetic Algorithms, and Particle Swarm Optimization achieve optimal results, while the others perform slightly better than random action selection. These findings demonstrate the potential of Particle Swarm Optimization and Simulated Annealing for efficient QRL learning, emphasizing the need for careful algorithm selection and adaptation.