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Quantum-enhanced Hybrid Convolution Regression Model for Sample Autonomous Vehicles
  • Haoyu Wang,
  • Landon Stobaugh,
  • William Stobaugh
Haoyu Wang

Corresponding Author:[email protected]

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Landon Stobaugh
William Stobaugh


Commercial demands for products of Artificial Intelligence are undeniably at its golden age in the modern world. Advances in physical hardware have provided enough computation capacity to train large machine-learning models for decent accuracy. Commercial products such as autonomous vehicles are demanding the growing specificity and complexity of machine learning models, trading off increasing model size for 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 harness the random nature of quantum mechanics to preserve quantum information and construct the basis for Variational Quantum Circuits (VQC). In this paper, we produce three convolution regression models to predict robot directions for a simplified autonomous-driving task and demonstrate a quantum advantage in our quantum-enhanced hybrid approach as compared to two classical models. Our results in this minimal example reflect the near-term feasibility of hybrid quantum models to enhance modern AI industries, which provides a promising vision to incorporate quantum computing in commercial products.
22 Jan 2024Submitted to TechRxiv
26 Jan 2024Published in TechRxiv