Toward the information-theoretic limit of programmable photonics

The scalability of many programmable photonic circuits is limited by the 2π tuning range needed for the constituent phase shifters. To address this problem, we introduce the concept of a phase-efficient circuit architecture, where the average phase shift is ≪2π. We derive a universal information-theoretic limit to the phase-shift efficiency of universal multiport interferometers and, based on a modified Mach–Zehnder Interferometer (MZI) design, propose a “3-MZI” architecture that approaches this limit to within a factor of 2×, approximately a 10× reduction in average phase shift over the prior art, where the average phase shift scales inversely with system size N as O(1/N). For non-unitary circuits, we show that the 3-MZI saturates the theoretical bound for Gaussian-distributed target matrices. Using this architecture, we show optical neural network training with all phase shifters constrained to ≲0.2 radians without loss of accuracy.