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在复杂的介质中基于量子神经网络的扭曲轨道角运动量束在复杂介质中的补偿
Gokul Manavalan1, Shlomi Arnon2
1Electrical and Computer Engineering department of Ben-Gurion University of the Negev, 8441405, Be'er Sheva, Israel. gokulm@post.bgu.ac.il.
Scientific reports
|December 2, 2025
概括
我们引入量子神经网络 (QNN),以利用轨道角动量 (OAM) 来弥补自由空间光通信中的扭曲. QNN实现了高精度,而经典代用品 (QqNN) 确保了实际应用的可扩展性.
科学领域:
- 量子信息科学 量子信息科学
- 光学通信是指光学通信.
- 机器学习 机器学习
背景情况:
- 自由空间光学网络利用轨道角动量 (OAM) 复合来增加容量.
- 大气动荡和散射会导致扭曲和交叉声,限制OAM的实际使用.
- 像卷积神经网络 (CNN) 这样的经典方法为这些退化提供了有限的补偿.
研究的目的:
- 研究使用变量量子神经网络 (QNN) 在现实的自由空间光学通道中对OAM束进行适应性补偿.
- 评估QNN在重建具有不同拓电荷的扭曲拉盖尔-高斯束的性能.
- 开发一个可扩展的QNN的经典替代品,以便在短期内部署.
主要方法:
- 实现变量量子神经网络 (QNN) 具有参数化的纠层,用于监督回归.
- 扭曲的拉盖尔-高斯束的端到端重建,具有拓电荷l ∈ {1,4,8,12}.
- 开发一个准量子神经网络 (QqNN),作为使用张量投影的量子动力学的经典模拟.
主要成果:
- QNN实现了平均平方误差低至4.0 × 10-6和SSIM高于0.99.
- 比特错误率被抑制了超过99.9%,达到0.0125%的BER.
- 准量子神经网络 (QqNN) 实现了接近最佳的性能,在降低复杂度的情况下实现了0.0375%的BER.
结论:
- 变量量子神经网络 (QNN) 提供了一个强大的,量子弹性解决方案,用于在具有挑战性的光学通道中适应OAM束补偿.
- 开发的准量子神经网络 (QqNN) 为短期部署提供了可扩展的经典替代方案,将量子和经典方法相结合.
- 这种混合框架推进了OAM解码,并展示了量子启发方法在光通信系统中的潜力.
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