Quantum Tree-Based Planning
André Sequeira, Luís Paulo Santos, Luís Soares Barbosa · IEEE Access · 2021
Reinforcement Learning is at the core of a recent revolution in the Artificial Intelligence community. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In this work, we reach for 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 with 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.