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arXiv 2103.04993cs.ETphysics.optics
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可编程光子学的硬件纠错

Hardware error correction for programmable photonics

Saumil Bandyopadhyay, Ryan Hamerly, Dirk Englund

摘要:

可重构干涉仪的可编程光子电路可用于实现对光学模式的任意操作,从而促进了用于加速量子模拟、信号处理和人工智能中的任务的灵活平台。按比例放大这些系统的主要障碍是静态制造误差,其中每个器件内的小元件误差累积以在电路计算内产生显著误差。减轻此误差通常需要依赖于来自电路的实时反馈的数值优化,这可能极大地限制硬件的可扩展性。在这里,我们提出了一个确定性的方法来纠正电路错误,通过本地纠正个别光闸内的硬件错误。我们应用我们的方法来模拟大规模的光学神经网络和无限脉冲响应滤波器中实现的可编程光子学,发现它们仍然弹性组件错误远远超出现代工艺公差。我们的研究结果突出了一个新的途径,可编程光子放大到数百个模式,在目前的制造工艺。

英文摘要:

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating this error usually requires numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here we present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our results highlight a new avenue for scaling up programmable photonics to hundreds of modes within current day fabrication processes.

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