Heavy tails and pruning in programmable photonic circuits for universal unitaries

Authors model programmable photonic circuits targeting universal unitaries and verify that a type of unit rotation operator has a heavy-tailed distribution. They suggest hardware pruning for random unitary and present design strategies for high fidelity and energy efficiency in large-scale quantum computations and photonic deep learning accelerators. 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. For the Clements design of programmable photonic circuits, 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.

Automated summary

The authors simulated programmable photonic circuits targeting universal unitaries and found a heavy-tailed distribution of a type of unit rotation operator. They suggested hardware pruning for random unitaries and provided design strategies for high fidelity and energy efficiency in large-scale quantum computations and photonic deep learning accelerators. Developing hardware for high-dimensional unitary operators plays a vital role in implementing quantum computations and deep learning accelerations.