Cryogenic in-memory computing using magnetic topological insulators

TL;DR

This paper introduces a cryogenic in-memory computing scheme using magnetic topological insulators as memristors, enabling efficient quantum and classical computations with high stability and low energy consumption.

Contribution

It presents a novel cryogenic computing approach leveraging magnetic topological insulators, demonstrating high accuracy and energy efficiency for neural networks and quantum tasks.

Findings

High energy efficiency and stability of magnetic topological memristors.

Successful classification in proof-of-concept experiments.

Lower energy consumption in large-scale neural network simulations.

Abstract

Machine learning algorithms have been proven effective for essential quantum computation tasks such as quantum error correction and quantum control. Efficient hardware implementation of these algorithms at cryogenic temperatures is essential. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a cryogenic in-memory computing scheme based on the coexistence of the chiral edge state and the topological surface state. The memristive switching and reading of the giant anomalous Hall effect exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks demonstrate software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing magnetic memristor and CMOS technologies. Our results not only showcase a new application of chiral edge states but also may inspire further topological quantum physics-based novel computing schemes.