Quantum reinforcement learning: the maze problem

Year: 2022

Authors: Dalla Pozza N.; Buffoni L.; Martina S.; Caruso F.

Autors Affiliation: Scuola Normale Super Pisa, Piazza Cavalieri 7, I-56126 Pisa, Italy; Univ Florence, Dept Phys & Astron, Via Sansone 1, I-50019 Sesto Fiorentino, Italy; LENS European Lab Nonlinear Spect, Via Carrara 1, I-50019 Sesto Fiorentino, Italy; QSTAR, I-50019 Sesto Fiorentino, Italy; INO, CNR, I-50019 Sesto Fiorentino, Italy.

Abstract: Quantum machine learning (QML) is a young but rapidly growing field where quantum information meets machine learning. Here, we will introduce a new QML model generalising the classical concept of reinforcement learning to the quantum domain, i.e. quantum reinforcement learning (QRL). In particular, we apply this idea to the maze problem, where an agent has to learn the optimal set of actions in order to escape from a maze with the highest success probability. To perform the strategy optimisation, we consider a hybrid protocol where QRL is combined with classical deep neural networks. In particular, we find that the agent learns the optimal strategy in both the classical and quantum regimes, and we also investigate its behaviour in a noisy environment. It turns out that the quantum speedup does robustly allow the agent to exploit useful actions also at very short time scales, with key roles played by the quantum coherence and the external noise. This new framework has the high potential to be applied to perform different tasks (e.g. high transmission/processing rates and quantum error correction) in the new-generation noisy intermediate-scale quantum (NISQ) devices whose topology engineering is starting to become a new and crucial control knob for practical applications in real-world problems. This work is dedicated to the memory of Peter Wittek.

Journal/Review: QUANTUM MACHINE INTELLIGENCE

Volume: 4 (1)      Pages from: 11-1  to: 11-10

More Information: This work was financially supported from Fondazione CR Firenze through the project QUANTUM-AI, the European Union´s Horizon 2020 research and innovation programme under FET-OPEN Grant Agreement No. 828946 (PATHOS), and from University of Florence through the project Q-CODYCES.
KeyWords: Quantum walks; Reinforcement learning; Quantum machine learning; Maze
DOI: 10.1007/s42484-022-00068-y

Citations: 9
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