使用量子真空噪声的光子概率机器学习
Seou Choi1, Yannick Salamin2,3, Charles Roques-Carmes4,5
1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA. seouc130@mit.edu.
Nature communications
|September 5, 2024
概括
研究人员开发了一台使用量子真空噪声进行机器学习的光子概率计算机. 这种新的硬件能够实现高速,节能的概率推断和图像生成,为先进的AI应用铺平了道路.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 光子学是指光子学的使用方法.
背景情况:
- 概率机器学习依赖于随机性来编码不确定性.
- 量子真空噪声提供了高速,节能随机性的来源.
- 有限的光子硬件存在,用于控制概率机器学习中的随机元素.
研究的目的:
- 使用新型光子概率神经元 (PPN) 实现光子概率计算机.
- 证明PPN在解决概率机器学习任务方面的能力.
- 为可扩展,超快速和节能全光学概率计算提出一条途径.
主要方法:
- 使用可视化光学参数振荡器 (OPO) 实现PPN,使用真空级注入偏差场.
- 用电子处理器 (FPGA或GPU) 编程时间复合PPN的测量和反循环.
- 利用量子真空噪声作为一种随机种子来编码不确定性并生成样本.
主要成果:
- 在MNIST手写数字上成功演示了概率推理和图像生成.
- 使用量子真空噪声编码分类不确定性和概率样本生成.
- 提出了一个全光学概率计算平台,采样速率为~1 Gbps,能源消耗为~5 fJ/MAC.
结论:
- 开发的光子概率计算机为机器学习提供了可扩展,超快,节能的硬件解决方案.
- 这项工作推动了量子现象与人工智能的集成,用于下一代计算.
- 拟议的全光学平台为概率计算带来了速度和能源效率的显著改善.
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