Quantum tree-based planning

• Main Author: Sequeira, Andre
• Publication Date: 2021
• Other Authors: Santos, Luís Paulo, Barbosa, L. S.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/1822/78050
• topic: Quantum computation; quantum reinforcement learning; sparse sampling; Planning; Heuristic algorithms; Quantum computing; Reinforcement learning; Qubit; Encoding; Quantum algorithm; Ciências Naturais::Ciências da Computação e da Informação; Science & Technology

Reinforcement Learning is at the core of a recent revolution in Arti cial Intelligence. Simultaneously, we are witnessing the emergence of a new  eld: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the  first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.