Linear Optical Random Projections Without Holography

Abstract

We introduce a novel method to perform linear optical random projections without the need for holography. Our method consists of a computationally trivial combination of multiple intensity measurements to mitigate the information loss usually associated with the absolute-square non-linearity imposed by optical intensity measurements. Both experimental and numerical findings demonstrate that the resulting matrix consists of real-valued, independent, and identically distributed (i.i.d.) Gaussian random entries. Our optical setup is simple and robust, as it does not require interference between two beams. We demonstrate the practical applicability of our method by performing dimensionality reduction on high-dimensional data, a common task in randomized numerical linear algebra with relevant applications in machine learning.

7 pages, 4 figures

References in corpus (3)

• Tuneable spin-glass optical simulator based on multiple light scattering
• Optical Transformers
• ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients

Cited by in corpus (1)

• Streamlined optical training of large-scale modern deep learning architectures with direct feedback alignment