Cryogenic In-Memory Computing Circuits with Giant Anomalous Hall Current in Magnetic Topological Insulators for Quantum Control

Cryogenic in-memory computing, operating at temperatures below 4.2 K, offers high performance and energy efficiency in computation-intensive environments, especially for quantum controls. Spintronic memristors based on anomalous Hall effect (AHE) can be utilized with advantages of multi-bit bipolar weights and nearly unlimited endurance. However, there exists two major challenges in realizing an AHE-based neural network (NN): first, anomalous Hall resistances $R_{\mathrm{H}}$ typically range in the order of a few ohms in conventional ferromagnets; second, signal-to-noise ratio of summation and read disturbance can lead to errors in NN operation. In this work, we demonstrate large $R_{\mathrm{H}}$ of 12 kΩ at 2 K using a magnetic topological insulator (MTI) utilizing the quantum AHE, and for the first time, propose and experimentally verify the multiply-and-accumulate operation of a transverse-read Hall-current based neural network (Hall NN). Simulation of the proposed MTI Hall NN achieves matching accuracy to a full-precision network in the noisy qubit state preparation task. Compared with the MRAM NN, the MTI NN features a 90% lower write energy and 10 times higher TOPS/W showing the promise of MTI Hall NNs.