Integrating Quantum-Assisted Computation into Deep Speech Enhancement Networks

Jicheng Yan, Ri‐Gui Zhou, Wenshan Xu +3 more

Rebh Soltani, Emna Benmohamed, Hela Ltifi

Harish Reddy Gantla, Hameed Miyan, G.Anil Kumar +3 more

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.

Paramita Basak Upama, Parama Sridevi, Shoumili Dutta +3 more

Fei Li, Guobiao Xu

Quantum Neural Network (QNN), a burgeoning new field which integrates quantum computation with classical neural network, can improve the inadequacies of artificial neural network. A model of quantum neuron and Quantum Back Propagation (BP) Neural Network based on quantum neuron are investigated. A novel approach toward speech enhancement which adopts Quantum BP Neural Network is proposed. The numerical simulation results show that the proposed speech enhancement scheme resulted in better performance than traditional spectral subtraction, Wiener filtering method and RBF neural network.

Dukuru Chiranjevi, Anisha Mishra, Ranjan Kumar Behera +1 more