通过基于梯度的学习策略,最大限度地提高多代理量子游戏的本地奖励.
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
概括
这项研究探讨了使用多个代理的量子游戏. 令人惊的是,低量子电路噪声可以提高复杂游戏的性能,为杂的量子计算机提供见解.
科学领域:
- 量子计算是一种量子计算.
- 游戏理论 游戏理论
- 人工智能的人工智能
背景情况:
- 多代理系统具有复杂的战略互动.
- 量子计算为游戏理论提供了新的方法.
- 噪音中等尺度量子 (NISQ) 设备具有固有的局限性.
研究的目的:
- 用基于梯度的策略在多代理环境中建模量子游戏.
- 分析介质的学习效率和量子噪声的影响.
- 为了研究量子电路噪声和算法性能之间的关系.
主要方法:
- 开发一个用于代理优化的学习模型.
- 模拟使用基于梯度的策略的代理.
- 分析不同级别量子电路噪声下的性能.
主要成果:
- 确定了量子电路噪声和算法性能之间的复杂关系.
- 增加的量子噪声通常会降低性能.
- 在特定条件下,低量子噪声可以提高大型多代理游戏的性能.
结论:
- 量子电路噪声对量子游戏性能有着非微不足道的影响.
- 这些发现对在游戏理论中利用NISQ计算机有意义.
- 这项研究强调了量子计算,游戏理论和强化学习交叉的机会.
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