The Goldilocks Principle of Learning Unitaries by Interlacing Fixed Operators with Programmable Phase Shifters on a Photonic Chip

Abstract

Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light. Here, we systematically investigate a general family of photonic circuits for realizing arbitrary unitaries based on a simple architecture that interlaces a fixed intervening layer with programmable phase shifter layers. We introduce a criterion for the intervening operator that guarantees the universality of this architecture for representing arbitrary $N\times N$ unitary operators with $N+1$ phase layers. We explore this criterion for different photonic components, including photonic waveguide lattices and meshes of directional couplers, which allows the identification of several families of photonic components that can serve as the intervening layers in the interlacing architecture. Our findings pave the way for efficiently designing and realizing novel families of programmable photonic integrated circuits for multipurpose analog information processing.

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Cited by in corpus (7)

• Learning Arbitrary Complex Matrices by Interlacing Amplitude and Phase Masks with Fixed Unitary Operations
• Integrated Photonic Programmable Random Matrix Generator with Minimal Active Components
• Photonic Matrix Multiplier Makes a Direction-Finding Sensor
• Near-optimal decomposition of unitary matrices using phase masks and the discrete Fourier transform
• Programmable Space-Frequency Linear Transformations in Photonic Interlacing Architectures
• Sub-quadratic scalable approximate linear converter using multi-plane light conversion with low-entropy mode mixers
• Embedding Matrices in Programmable Photonic Networks with Flexible Depth and Width