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127. Quantum Tree-Based Planning

Author

Luís Paulo Santos, Luís Soares Barbosa, Andre Sequeira, and Universidade do Minho

Subjects
Speedup, Theoretical computer science, General Computer Science, Quantum machine learning, Computer science, 020209 energy, 02 engineering and technology, Reinforcement learning, Quantum computation, 0202 electrical engineering, electronic engineering, information engineering, Heuristic algorithms, State space, General Materials Science, Electrical and Electronic Engineering, Time complexity, Quantum computer, Science & Technology, General Engineering, Ciências Naturais::Ciências da Computação e da Informação, quantum reinforcement learning, Quantum computing, TK1-9971, sparse sampling, Planning, Qubit, Encoding, Quantum algorithm, 020201 artificial intelligence & image processing, Electrical engineering. Electronics. Nuclear engineering
Abstract

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., This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.

Published
2021
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