Related Experiment Video
Updated: Jul 10, 2025
05:30
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
584
Maximizing Local Rewards on Multi-Agent Quantum Games through Gradient-Based Learning Strategies.
Agustin Silva1, Omar Gustavo Zabaleta1, Constancio Miguel Arizmendi1
1ICYTE (Instituto de Investigaciones Científicas y Tecnológicas), Mar del Plata B7600, Argentina.
Entropy (Basel, Switzerland)
|November 24, 2023
Summary
This study explores quantum games with multiple agents. Surprisingly, low quantum circuit noise can improve performance in complex games, offering insights for noisy quantum computers.
Area of Science:
- Quantum Computing
- Game Theory
- Artificial Intelligence
Background:
- Multi-agent systems present complex strategic interactions.
- Quantum computation offers novel approaches to game theory.
- Noisy Intermediate-Scale Quantum (NISQ) devices have inherent limitations.
Purpose of the Study:
- To model quantum games in multi-agent settings using gradient-based strategies.
- To analyze the learning efficacy of agents and the impact of quantum noise.
- To investigate the relationship between quantum circuit noise and algorithm performance.
Main Methods:
- Development of a learning model for agent optimization.
- Simulation of agents employing gradient-based strategies.
- Analysis of performance under varying levels of quantum circuit noise.
Main Results:
- A complex relationship between quantum circuit noise and algorithm performance was identified.
- Increased quantum noise generally degrades performance.
- Under specific conditions, low quantum noise can enhance performance in large multi-agent games.
Conclusions:
- Quantum circuit noise has a non-trivial effect on quantum game performance.
- Findings have implications for utilizing NISQ computers in game theory.
- The study highlights opportunities at the intersection of quantum computing, game theory, and reinforcement learning.
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