A Superconducting Flux-Quanta Memory Device for Cryogenic Neuromorphic and Probabilistic Computing

As quantum computing progresses toward large-scale implementation, there is a growing demand for cryo-compatible artificial intelligence hardware and computing paradigms that accelerate the quantum control and error correction. Here, we present a superconducting memory device composed of a flux-quanta storage loop and a superconducting quantum interference device (SQUID), functioning as an artificial synapse and a binary stochastic neuron, respectively. Information is encoded in the stochastic behavior of the pulse stream passing through the neuron and is extracted by counting the number of SQUID switching events. In this scheme, sampling precision is flexibly tunable by adjusting the total number of input pulses. This architecture enables both neuromorphic and probabilistic computing, achieving 100% accuracy in nine-pixel image classification and integer factorization tasks by tuning the sampling precision. Furthermore, we found that the fabrication non-uniformities of up to 7.1% can be effectively compensated by an increased coupling strength. These results highlight the potential of superconducting memory devices as scalable and robust building blocks for implementing versatile AI computing paradigms under cryogenic conditions.