实现一个用于实时量子反的深度强化学习代理
Kevin Reuer1,2, Jonas Landgraf3,4, Thomas Fösel3,4
1Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. kevin.reuer@phys.ethz.ch.
Nature communications
|November 6, 2023
概括
我们开发了一种新的,低延迟的强化学习代理,用于实时量子设备控制. 这种人工智能系统有效地初始化超导量子位,仅使用测量反.
科学领域:
- 量子技术是一种量子技术.
- 人工智能的人工智能是人工智能.
- 控制系统 控制系统
背景情况:
- 精确的实时控制对于量子技术至关重要,需要比一致性时间更快的操作.
- 无模型的强化学习 (RL) 提供了一种途径,可以在没有系统模型的情况下发现控制策略.
- 为量子系统实施实时,反驱动的RL仍然是一个重大挑战.
研究的目的:
- 实施强化学习代理来实时控制单个量子比特.
- 为了证明该代理在高效初始化超导量子比特的能力.
- 克服开发和培训RL代理人用于低延迟反系统的挑战.
主要方法:
- 在现场可编程网关阵列 (FPGA) 上开发了一个微秒以下延迟的神经网络代理.
- 利用无模型的强化学习来训练代理.
- 用户基于测量的反用于代理培训和量子比特初始化.
主要成果:
- 成功实施了用于单量子比特控制的实时增强学习代理.
- 使用RL代理证明了超导量子位的高效初始化.
- 在神经网络反循环中实现了微秒以下的延迟.
结论:
- 这项工作为量子控制中实时,无模型的强化学习提供了一种可行的方法.
- 实现的基于FPGA的代理方便了高效的超导量子比特初始化.
- 这代表了整合RL用于控制量子设备和其他低延迟反系统的重要一步.
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