A hybrid classical-quantum approach to speed-up Q-learning.
Sci Rep
; 13(1): 3913, 2023 Mar 08.
Article
in En
| MEDLINE
| ID: mdl-36890198
ABSTRACT
We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. Using the paradigm of quantum accelerators, we introduce a routine that runs on a quantum computer, which allows for the encoding of probability distributions. This quantum routine is then employed, in a reinforcement learning set-up, to encode the distributions that drive action choices. Our routine is well-suited in the case of a large, although finite, number of actions and can be employed in any scenario where a probability distribution with a large support is needed. We describe the routine and assess its performance in terms of computational complexity, needed quantum resource, and accuracy. Finally, we design an algorithm showing how to exploit it in the context of Q-learning.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Sci Rep
Year:
2023
Document type:
Article
Affiliation country:
Italy