Quantum-Enhanced Spiking Neural Networks for Closed-Loop Neuromodulation Systems: A Theoretically Advanced Framework

Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. Simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neuromorphic computing, providing a novel method enabling significant improvement of precision and efficiency in neuromodulation therapy for neurological disorders. As a future tool for our understanding of neuroscience, quantum technology and biomedical engineering, the QESNN framework has the potential for large societal impact.