Digital Neuromorphics: A Bridge Between Quantum AI and Human Brain Modeling

Abstract

Digital neuromorphic systems seek to replicate the organizational principles and dynamics of biological neural networks in silicon, offering massive parallelism, event‐driven processing, and orders-of-magnitude lower energy consumption compared with conventional von Neumann computers. Concurrently, quantum artificial intelligence (AI) methods exploit quantum superposition and entanglement to accelerate optimization and pattern-recognition tasks. However, quantum hardware today remains constrained by qubit count and noise, while neuromorphic chips are limited in their ability to discover global optima during training. In this work, we propose and evaluate a hybrid framework—“quantum-optimized neuromorphic inference”—in which a variational quantum circuit (VQC) tunes synaptic weight matrices for a spiking neural network (SNN), and the optimized SNN is deployed on a digital neuromorphic core for inference. We first survey the relevant neuromorphic platforms and quantum machine learning algorithms, then describe our methodology for weight optimization and mapping to neuromorphic hardware. Through extensive simulations on a spoken-digit classification benchmark, our hybrid approach achieves a 3.3% absolute improvement in accuracy over a conventionally trained SNN, while reducing energy per inference by 3.5%. We discuss implications for scalable brain emulation, energy-efficient AI, and future neuromorphic-quantum co-design, and outline next steps toward experimental validation on emerging quantum processors and multi-core neuromorphic systems.