基于可重新配置的光子神经网络的矩阵固有值解决器
Kun Liao1, Chentong Li1, Tianxiang Dai1
1State Key Laboratory for Mesoscopic Physics & Department of Physics, Collaborative Innovation Center of Quantum Matter, Beijing Academy of Quantum Information Sciences, Nano-optoelectronics Frontier Center of Ministry of Education, Peking University, Beijing 100871, China.
Nanophotonics (Berlin, Germany)
|December 5, 2024
概括
研究人员开发了一个光子神经网络来解决矩阵固有值,在4x4矩阵中达到78.8%的准确性. 这一突破使得芯片上的自值计算速度更快,更高效.
科学领域:
- 数字分析 数字分析
- 这是光子计算.
- 人工智能的人工智能是人工智能.
背景情况:
- 自身价值问题在工程和科学中至关重要.
- 现有的算法缺乏光子实现.
- 光子神经网络提供速度和低能耗.
研究的目的:
- 为实值对称矩阵自值解决提出光子神经网络.
- 在光子平台上展示可行性.
- 启用芯片上集成的全光学计算.
主要方法:
- 使用可重新配置的光子神经网络.
- 使用石墨烯/Si热光学调制.
- 包含一个和吸收非线性激活层.
- 在2x2,3x3和4x4矩阵上进行测试.
主要成果:
- 证明了对实值对称矩阵的固有值解决方法.
- 在4x4矩阵中获得了78.8%的实验准确度.
- 理论上预测了2x2矩阵的93.6%准确度.
结论:
- 拟议的策略对于芯片上的光子自值解决器是可行的.
- 这项工作为下一代全光学计算奠定了基础.
- 能够实现高效和高速的矩阵计算.
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