Emerging Quantum Materials for Neuromorphic Computing: From Fundamental Physics to Device Architectures

Abstract

The explosive growth of artificial intelligence (AI), edge computing, and brain-inspired algorithms has spurred the development of neuromorphic systems that mimic biological information processing. Achieving such functionality at the hardware level requires materials that exhibit neuron- and synapse-like behaviors in a scalable, low-power, and CMOS-compatible manner. In this review, we provide a comprehensive assessment of emerging quantum materials including Mott insulators, phase change materials (PCMs), topological insulators (TIs), twodimensional (2D) materials, and ferroelectrics highlighting their unique physical mechanisms and their relevance to neuromorphic device operation. Each material class is examined in terms of its electronic properties, switching dynamics, and compatibility with spiking neural networks (SNNs) and in-memory computing architectures. We compare their performance across key metrics such as energy efficiency, analog programmability, synaptic plasticity, and integration scalability. Furthermore, we engage in a comprehensive discussion regarding device prototypes that are predicated upon quantum materials, delineate the contemporary challenges associated with integration, and provide insights into hardware–algorithm co-design methodologies. This review additionally recognizes nascent trends such as hybrid material heterostructures, quantumclassical neuromorphic frameworks, and bioinspired learning paradigms that leverage intrinsic material dynamics. Our examination underscores that the amalgamation of the distinctive functionalities inherent in quantum materials with neuromorphic hardware presents a promising trajectory towards mitigating the constraints imposed by traditional computing paradigms. This synthesis facilitates the development of quantum neuromorphic platforms capable of real-time learning, operating with minimal energy consumption, and possessing adaptive learning architectures that dynamically adjust in accordance with cognitive requirements. Such platforms are poised to usher in the subsequent generation of computing systems that can rival or potentially surpass the performance of conventional von Neumann architectures.