Quantum-Enhanced Neuromorphic Computing for Real-World Applications in Vision Speech Healthcare and Smart Cities

Smart systems deployed in vision, speech, healthcare, and smart city settings are increasingly working under severe requirements of latency, energy use and uncertainty, revealing inherent weaknesses in standard deep learning methods, as well as in single- shots of quantum computing. Although both neuromorphic computing and quantum-enhanced learning have been demonstrated to be promising, independently, neither approach can provide a tradeoff between real-time performance and global reasoning. This paper discusses the absence of a single and deployment focused model that would bring neuromorphic efficiency and quantum expressiveness to a single system. In our proposal, a quantum-enhanced neuromorphic computing system, event-driven spiking neural networks are used to do continuous, low-power perception and quantum-assisted machinery used to invoke uncertainty-aware classification and global optimization.