编织的干扰仪网用于强大的光子矩阵-矢量乘法与非理想的组件
Optics express
|January 29, 2025
概括
与现有设计相比,新的光子干扰仪架构为矩阵向量乘法提供了更高的稳定性和性能. 这一创新对于可扩展的光子神经形态计算至关重要,即使存在不可避免的缺陷.
科学领域:
- 光学和光学工程的光学和光学工程.
- 量子计算和机器学习硬件
- 干涉测量器件的设计设计.
背景情况:
- 矩阵向量乘法 (MVM) 是机器学习和量子计算中的基本操作.
- 光子架构为线性操作提供高速,低延迟和最小损失.
- 像克莱门茨和弗尔兹扬这样的现有设计在稳定性和强度方面面临着可扩展性挑战.
研究的目的:
- 介绍和评估MVM的新型光子干扰仪架构.
- 将架构的性能与克莱门茨和弗尔兹扬的设计进行比较.
- 在现实的非理想条件下评估稳定性和可扩展性.
主要方法:
- 数字模拟用于评估光子干扰仪架构.
- 系统地引入非理想:插入损失,光束分割器不平衡,交叉声.
- 布雷德,克莱门茨和弗尔兹扬设计的性能,足迹和插入损失分析.
主要成果:
- 光子架构表现出比克莱门茨和弗尔德吉安的设计更优越的稳定性和性能.
- 编织架构显示了增强的可扩展性和更好的性能,随着干扰仪尺寸的增加.
- 尽管由于过境造成的足迹和损失略有增加,但最近的技术进步减轻了这些影响.
结论:
- 光子干扰仪是用于大规模光子神经形态计算的强大和高保真解决方案.
- 它的对称设计和减少的层数有助于在现实的,不完美的条件下提供卓越的性能.
- 这种架构代表了光子线性运算在苛刻应用中的重大进步.
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