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Quantum Machine Learning
Quantum Agents for Algorithmic Discovery
arXiv
Authors: Iordanis Kerenidis, El-Amine Cherrat
Year
2025
Paper ID
51513
Status
Preprint
Abstract Read
~2 min
Abstract Words
95
Citations
N/A
Abstract
We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search algorithm; optimal cheating strategies for strong coin flipping; and optimal winning strategies for the CHSH and other nonlocal games. The agents achieve these results directly through interaction, without prior access to known optimal solutions. This demonstrates the potential of quantum intelligence as a tool for algorithmic discovery, opening the way for the automated design of novel quantum algorithms and protocols.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols.
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