Hybrid Quanvolutional Regression Neural Network: Quantum Advantage for Near-term Autonomous Machine

摘要

Modern progress in computing power provides great capacity to train large machine-learning models for sufficient accuracy demanded by commercial products. Commercial products such as autonomous vehicles are demanding the complexity of neural networks, traditionally increasing model size to achieve high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms (Variational Quantum Circuits) harness the random nature of quantum mechanics to preserve quantum information and improve model training by allowing parallel computing. This paper demonstrates a hybrid methodology implementing a Hybrid Quantum Neural Network (HQNN) to achieve quantum advantage for a convolutional regression model. The resulting hybrid model performance shows an immediate reduction in model complexity and improvement in training efficiency for the autonomous robot, this contrast with the classical control model provides a promising approach for the near-term feasibility of quantum-enhanced ML products for AI industries.