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The study focuses on adding QNNs to 7G wireless technology to enable better and safer connections for autonomous cars. We use ten recent, major studies to look at how quantum-based machine learning has led to better performance in terms of BER, energy savings, and fast decisions in vehicular and UAV systems. Five graphs show how QNNs are better than other types of networks on key applications, including STAR-RIS-assisted VRCS and RIS-NOMA 6G/7G systems. We observed that QNNs can decrease over 35% of the estimation errors and make the system work and scale better in fluctuating situations. We include literature review, examination of algorithms, picking out metrics, using simulation, and creating organized diagrams in our work. The results highlight how QNNs play a key role in making URLLC useful in important IoT and V2X environments. The researchers finish the study by discussing issues related to deployment and suggest future work in improving quantum–classical hybrid optimization, creating standards, and enhancing security.

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Leveraging Quantum Neural Networks for Next-Generation 7G-Based Autonomous Vehicle Connectivity

  • 章节

  • 发表日期：

    2026年

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  • N. V. Krishna Rao,
  • Panduraju Pagidimalla,
  • Sree Vaishnavi Karipe,
  • Bharath Chikkudu

摘要

The study focuses on adding QNNs to 7G wireless technology to enable better and safer connections for autonomous cars. We use ten recent, major studies to look at how quantum-based machine learning has led to better performance in terms of BER, energy savings, and fast decisions in vehicular and UAV systems. Five graphs show how QNNs are better than other types of networks on key applications, including STAR-RIS-assisted VRCS and RIS-NOMA 6G/7G systems. We observed that QNNs can decrease over 35% of the estimation errors and make the system work and scale better in fluctuating situations. We include literature review, examination of algorithms, picking out metrics, using simulation, and creating organized diagrams in our work. The results highlight how QNNs play a key role in making URLLC useful in important IoT and V2X environments. The researchers finish the study by discussing issues related to deployment and suggest future work in improving quantum–classical hybrid optimization, creating standards, and enhancing security.