量子储库计算机的同步化
Xiaoyong Wu1, Xiaohua Cai1, Tongfeng Weng1
1Institute of Information Economy and Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China.
Chaos (Woodbury, N.Y.)
|December 4, 2025
概括
这项研究表明,量子储库计算 (QRC) 可以学习混乱系统. 我们展示了QRC能够在多个量子系统之间进行同步预测,这对于复杂的动态至关重要.
科学领域:
- 量子计算是一种量子计算.
- 混沌理论 混沌理论
- 非线性动力学是一种非线性动力学.
背景情况:
- 量子储库计算 (QRC) 显示出对复杂系统建模的前景.
- 学习混乱系统动态是具有挑战性的,因为它们固有的不可预测性.
研究的目的:
- 调查QRC系统中的同步,以学习混乱动态.
- 建立一个驱动-响应框架,使用QRC进行同步预测.
- 分析合强度对同步性能的影响.
主要方法:
- 训练QRC模型来学习混乱系统的动态方程.
- 开发了一个驱动-响应同步框架,使用两个独立训练的QRC模型.
- 通过分析预测值和系统变化的合强度之间的欧几里德距离来评估同步.
主要成果:
- 确认QRC能够捕捉非线性时间序列动态的能力.
- 证明了两个独立的QRC模型之间的成功同步.
- 确定了合强度在同步演变中的关键作用.
结论:
- QRC是模拟混乱系统的可行工具.
- 在多个独立的量子储库系统中,同步预测是可行的.
- 拟议的同步机制为复杂系统分析提供了新的可能性.
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