Summary of Reinforcement Learning For Quantum Tiq-taq-toe, by Catalin-viorel Dinu and Thomas Moerland
Reinforcement learning for Quantum Tiq-Taq-Toe
by Catalin-Viorel Dinu, Thomas Moerland
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel application of reinforcement learning (RL) to Quantum Tiq-Taq-Toe, a well-known benchmark for quantum computing and machine learning. The authors aim to integrate RL with quantum computing in this complex game, which may serve as an accessible testbed for the integration of both fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Quantum Tiq-Taq-Toe is a special kind of game that’s great for testing new ways to use computers that work with tiny particles called qubits. Right now, scientists are using these computers to play games and solve problems. But they haven’t tried using a special way of training the computer to make good moves, called reinforcement learning. This paper is about trying that approach in Quantum Tiq-Taq-Toe. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
