High-dimensional quantum key distribution (QKD) is a popular protocol that provides information theoretically secure keys to multiple parties. Two important steps of QKD are 1) the information reconciliation (IR) step, where parties reconcile mismatches in generated keys through classical communication, and 2) the privacy amplification (PA) step, where parties distill their common key into a new secure key that the adversary has little to no information about. In general, these two steps have been abstracted as two distinct problems. In this work, we design our IR protocol to be aware of the PA step and utilize sampling to relax the requirement on the IR step without sacrificing the final key length of the PA step, allowing for more bits generated in key creation utilizing practical decoders. We provide a novel PA-aware LDPC code construction known as Block-MDS QC-LDPC codes that can utilize the relaxed requirement. We demonstrate through simulations that our technique of sampling can provide notable gains in successfully creating secret keys.

The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This ‘free’ convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28× better energy-delay product compared to state-of-art photonic neural network accelerators.

In recent years, there has been a significant focus on achieving low-latency and high-throughput convolutional neural network (CNN) inference. Integrated photonics offers the potential to substantially expedite neural networks due to its inherent low-latency properties. Recently, on-chip Fourier optics-based neural network accelerators have been demonstrated and achieved superior energy efficiency for CNN acceleration. By incorporating Fourier optics, computationally intensive convolution operations can be performed instantaneously through on-chip lenses at a significantly lower cost compared to other on-chip photonic neural network accelerators. This is thanks to the complexity reduction offered by the convolution theorem and the passive Fourier transforms computed by on-chip lenses. However, conversion overhead between optical and digital domains and memory access energy still hinder overall efficiency.