Quantum-Inspired Edge AI Optimization for Neural Prosthetics

Abstract

Quantum-inspired optimization algorithms have emerged as a compelling alternative to fully quantum approaches, leveraging classical hardware to simulate key quantum features—such as superposition, entanglement analogs, and parameterized variational circuits—to solve high-dimensional, nonconvex optimization problems more efficiently than traditional heuristics. This manuscript presents Quantum-Inspired Edge AI Optimization for Neural Prosthetics (QEAI-O-NP), a comprehensive framework that integrates a Variational Quantum-Inspired Optimizer (VQIO) with a Spiking Neural Network (SNN) decoder, both tailored for on-device execution on resource-constrained edge platforms. Combining a hybrid quantum-classical metaheuristic surrogate of the Quantum Approximate Optimization Algorithm (QAOA) with low-power neuromorphic inference, QEAI-O-NP addresses challenges of adaptive weight tuning, latency, and energy efficiency inherent to real-time electromyographic (EMG) signal decoding for prosthetic control. The framework preprocesses multi-channel EMG via bandpass filtering and windowing, encodes features into spike trains, and performs classification through a three-layer LIF-based SNN. Periodically, the VQIO formulates the current loss landscape as a cost Hamiltonian and executes two-layer QAOA-inspired tensor-network simulations to extract gradient estimates for synaptic weight updates. Deployed on an NVIDIA Jetson Xavier NX, QEAI-O-NP achieves a 15% absolute gain in decoding accuracy (from 80.2% to 95.2%), reduces inference latency by 30% (from 45.8 ms to 32.1 ms), and cuts energy consumption per inference by 25% (from 193 mJ to 145 mJ) compared to state-of-the-art edge AI baselines. An ablation study explores the trade-offs of QAOA depth and update frequency, guiding practical parameter selection.