Training continuously-coupled reconfigurable photonic chips with quantum machine learning

TL;DR

This paper introduces a machine learning approach to precisely program reconfigurable integrated photonic interferometers, especially those with continuously-coupled waveguides, using limited measurements and without detailed internal modeling.

Contribution

It develops a novel black box machine learning methodology for programming continuously-coupled photonic circuits, filling a gap in current reconfigurable interferometer control techniques.

Findings

The method is effective across various waveguide layouts.

It requires only limited single- and two-photon measurements.

The approach is robust and suitable for optical quantum information processing.

Abstract

Integrated photonic technologies have recently shown significant advances, enabling the possibility to implement reconfigurable interferometers with increasing size. One of the main tasks to fully exploit the capabilities of reconfigurable integrated interferometers is the possibility to precisely program their operation to perform a desired target unitary. While recipes are known for circuit layouts based on a cascade of beam-splitter and phase-shifter operations, a methodology applicable for reconfigurable continuously-coupled waveguide arrays is currently missing. Here, we devise a machine learning based approach for this task, using a black box methodology that does not rely on precise a-priori modeling of the circuit internal architectures. We verify the effectiveness and the robustness of this approach via numerical simulations on different continuously-coupled waveguides layouts, either with planar or 3D structures. The proposed method makes use of a limited number of single- and two-photon measurements, making it suitable for optical quantum information processing. The obtained results open the perspective of employing this methodology as an effective tool to program the operation of integrated interferometers designed via different architectures.