Matrix Optimization on Universal Unitary Photonic Devices

Sunil Pai, Ben Bartlett, Olav Solgaard, David A.B. Miller

Research output: Contribution to journalArticlepeer-review

Abstract

Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of input modes and provide a promising hardware platform for fast and energy-efficient machine learning using light. We simulate the gradient-based optimization of random unitary matrices on universal photonic devices composed of imperfect tunable interferometers. If device components are initialized uniform randomly, the locally interacting nature of the mesh components biases the optimization search space toward banded unitary matrices, limiting convergence to random unitary matrices. We detail a procedure for initializing the device by sampling from the distribution of random unitary matrices and show that this greatly improves convergence speed. We also explore mesh architecture improvements such as adding extra tunable beam splitters or permuting waveguide layers to further improve the training speed and scalability of these devices.

Original languageEnglish
Article number064044
JournalPhysical Review Applied
Volume11
Issue number6
DOIs
Publication statusPublished - 19 Jun 2019
Externally publishedYes

Fingerprint

Dive into the research topics of 'Matrix Optimization on Universal Unitary Photonic Devices'. Together they form a unique fingerprint.

Cite this