Quantum reinforcement learning Comparing quantum annealing and gate-based quantum computing with classical deep reinforcement learning

N.M.P. Neumann*, P.B.U.L. de Heer, F. Phillipson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by introducing a new learning technique called curriculum learning. With curriculum learning, we gradually increase the complexity of the environment and we find that it has a positive effect on the expected reward of a traversal. We see that the number of training steps needed for the two quantum approaches is lower than that needed for the classical approach.
Original languageEnglish
Article number125
Number of pages18
JournalQuantum Information Processing
Issue number2
Publication statusPublished - 22 Feb 2023


  • Quantum computing
  • Gate-based quantum computing
  • Annealing-based quantum computing
  • Quantum annealing
  • Reinforcement learning
  • Grid traversal

Cite this