量子噪声有限的光学神经网络在每次激活时运行几个量子
Shi-Yuan Ma1, Tianyu Wang1, Jérémie Laydevant1,2
1School of Applied and Engineering Physics, Cornell University, Ithaca, 14853, NY, USA.
Research square
|November 14, 2023
概括
光学神经网络可以在使用单个光子的MNIST分类上达到98%的准确性,尽管量子噪声很高. 这种超低功耗的方法每次使用0.003 attojoules,为节能AI铺平了道路.
科学领域:
- 人工智能的人工智能
- 量子计算是一种量子计算.
- 光学工程是指光学工程.
背景情况:
- 由于量子噪声,计算中的能源效率面临着根本的限制.
- 模拟物理神经网络提供了速度和效率的潜力,但通常需要高的信号噪声比率 (>10).
- 以前的低能光学神经网络没有达到高精度.
结论:
- 即使在极端量子噪声条件下,精确的机器学习推断也是可行的.
- 在光学神经网络中单光子运行代表了朝着高能效的人工智能硬件的有希望的道路.
- 开发的培训方法可能适用于其他超低功耗计算范式.
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