More with less: fault-tolerance and information-theoretic optimality in programmable photonics

To scale up programmable photonic circuits, one must overcome challenges of size, power, and robustness to hardware imperfections. Considering linear multiport interferometers as a concrete example, we show how architectural choices can significantly improve the prospects for scalability. First, we review the field of photonic-circuit error correction, and show how 3-splitter MZI (3-MZI) architectures are more robust to errors than standard MZI architectures, even achieving asymptotic fault-tolerance to hardware imperfections. Second, we discuss the problem of economizing phase-shift, which is especially relevant for power-hungry thermo-optic platforms, and show how the 3-MZI architecture can reduce average phase shifts by a factor of 3-10x in near-term systems.

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Published
2024-10-02
DOI
https://doi.org/10.1117/12.3029941
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

More with less: fault-tolerance and information-theoretic optimality in programmable photonics

Ryan Hamerly
Neural Networks and Reservoir Computing
article

More with less: fault-tolerance and information-theoretic optimality in programmable photonics

Ryan Hamerly
article en

Abstract

To scale up programmable photonic circuits, one must overcome challenges of size, power, and robustness to hardware imperfections. Considering linear multiport interferometers as a concrete example, we show how architectural choices can significantly improve the prospects for scalability. First, we review the field of photonic-circuit error correction, and show how 3-splitter MZI (3-MZI) architectures are more robust to errors than standard MZI architectures, even achieving asymptotic fault-tolerance to hardware imperfections. Second, we discuss the problem of economizing phase-shift, which is especially relevant for power-hungry thermo-optic platforms, and show how the 3-MZI architecture can reduce average phase shifts by a factor of 3-10x in near-term systems.

NTT (United States) (US)
Openalex Percentile: Top 85%
Neural Networks and Reservoir Computing
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