动态估计的概率密度使用量子神经网络基于简单的和振荡器扰乱电场的电场密度
Guguloth Sagar1, Harish Parthasarathy1, Vijyant Agarwal1
1Netaji Subhas University of Technology, New Delhi, India.
Cognitive neurodynamics
|August 15, 2025
概括
这项研究介绍了一种量子神经网络,该网络控制一个量子波器.
科学领域:
- 量子力学就是量子力学.
- 计算物理学的计算物理.
- 机器学习 机器学习
背景情况:
- 量子波器是量子力学的基本系统.
- 控制量子系统对于量子计算和仿真至关重要.
- 概率密度函数 (PDF) 描述了量子状态分布.
研究的目的:
- 为控制量子系统提出一个量子神经网络 (QNN).
- 为了证明在施罗丁格方程中产生时间变化的波函数.
- 使用可控量子波器跟踪目标概率密度函数 (PDF).
主要方法:
- 使用被电场扰乱的量子波器.
- 采用一个随机梯度算法来控制电场适应.
- 应用扰动理论进行统计性能分析.
主要成果:
- 通过控制电场,成功生成了时间变化的波动函数.
- 波函数的模量方程跟踪给定的PDF.
- 分析控制电场动态和统计性能.
结论:
- 一种新的QNN方法可以精确控制量子波器.
- 这种方法允许合成目标概率密度函数.
- 潜在的应用包括数据压缩和PDF合成 (例如,来自语音的EEG).
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