Heavy tails and pruning in programmable photonic circuits

Abstract

Developing hardware for high-dimensional unitary operators plays a vital role in implementing quantum computations and deep learning accelerations. Programmable photonic circuits are singularly promising candidates for universal unitaries owing to intrinsic unitarity, ultrafast tunability, and energy efficiency of photonic platforms. Nonetheless, when the scale of a photonic circuit increases, the effects of noise on the fidelity of quantum operators and deep learning weight matrices become more severe. Here we demonstrate a nontrivial stochastic nature of large-scale programmable photonic circuits-heavy-tailed distributions of rotation operators-that enables the development of high-fidelity universal unitaries through designed pruning of superfluous rotations. The power law and the Pareto principle for the conventional architecture of programmable photonic circuits are revealed with the presence of hub phase shifters, allowing for the application of network pruning to the design of photonic hardware. We extract a universal architecture for pruning random unitary matrices and prove that "the bad is sometimes better to be removed" to achieve high fidelity and energy efficiency. This result lowers the hurdle for high fidelity in large-scale quantum computing and photonic deep learning accelerators.

References in corpus (8)

• Power-law distributions in empirical data
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Photonics for artificial intelligence and neuromorphic computing
• Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
• Quantum circuits with many photons on a programmable nanophotonic chip
• Prospects and applications of photonic neural networks
• Stability of Self-Configuring Large Multiport Interferometers
• Fidelity Between Unitary Operators and the Generation of Gates Robust Against Off-Resonance Perturbations

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