Bakhrom Oripov

The brain implements recurrent neural networks (RNNs) efficiently, and modern computing hardware does not. Although specialized neuromorphic hardware is well suited for recurrent implementations in the inference phase, it does not lend itself to the primary training method for RNNs, backpropagation through time (BPTT), for on-chip learning.

, Dana Rampini, Jason Allmaras, Matt Shaw, , Boris Korzh, For the past 50 years, superconducting detectors have offered exceptional sensitivity and speed for detecting faint electromagnetic signals in a wide range of applications. These detectors operate at very low temperatures and generate a minimum of excess noise, making them ideal for testing the non-local nature of reality, investigating dark matter, mapping the early universe and performing quantum computation and communication.

, Dana Rampini, Jason Allmaras, Matt Shaw, , Boris Korzh, Over the last 20 years, superconducting nanowire single-photon detectors (SNSPD) have proven to be a highly effective photon-counting technology, with very high detective efficiencies, low dark counts, and low jitter. However, cameras based on SNSPDs still have relatively low pixel counts, due to difficulties multiplexing their low-amplitude, high-bandwidth signals.