Superconducting optoelectronic networks for neuromorphic supercomputing

Superconducting optoelectronic networks are a promising route to large-scale neuromorphic computing. Using light for communication at the single-photon level overcomes fan-out challenges of electrical communication while achieving the minimum possible latency and lowest possible light levels. Such communication can be achieved with synapses based on superconducting single-photon detectors. Performing computations with superconducting Josephson junctions offers simple instantiations of synaptic, dendritic, and neuronal functions, and Josephson junctions have the highest speed over energy quotient of any known active circuit element. There is no known method to compute faster with less energy. This talk will summarize experimental demonstrations of single-photon communication links in silicon photonics, multiplanar waveguide routing networks, single-photon synapses and their associated memory circuits, optoelectronic dendrites, and superconductor-semiconductor interfaces that drive the light sources. Training algorithms that make such networks useful for artificial intelligence applications will be presented.