Referenceless characterization of complex media using physics-informed neural networks

Abstract

In this work, we present a method to characterize the transmission matrices of complex scattering media using a physics-informed, multi-plane neural network (MPNN) without the requirement of a known optical reference field. We use this method to accurately measure the transmission matrix of a commercial multi-mode fiber without the problems of output-phase ambiguity and dark spots, leading to up to 58% improvement in focusing efficiency compared with phase-stepping holography. We demonstrate how our method is significantly more noise-robust than phase-stepping holography and show how it can be generalized to characterize a cascade of transmission matrices, allowing one to control the propagation of light between independent scattering media. This work presents an essential tool for accurate light control through complex media, with applications ranging from classical optical networks, biomedical imaging, to quantum information processing.

Reconfigurable quantum-optical circuits in a complex medium

“…Experimentally, we implement this architecture by placing a multi-mode fibre (MMF) between a pair of spatial light modulators (SLMs) to program general unitary circuits as shown in Fig 1b . After recovering U 1 by employing multi-plane neural networks (MPNN), 6 we perform the wavefront matching (WFM) algorithm , 7 which is an inverse design technique to encode a desired circuit T in our experiment. We implement a variety of different target circuits including the identity-I, high-dimensional analogs of Pauli-Z and X, Fourier-F, and random unitaries-R in dimensions d = {2, 3, 5, 7} manipulating states in the macro-pixel and the orbital-angularmomentum basis.…”