# Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications

arXiv: https://arxiv.org/abs/2401.01370 · HTML: https://mlsanity.com/paper/2401.01370/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Entanglement Induced Barren Plateaus](https://mlsanity.com/paper/2010.15968/) (2020)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Provably efficient machine learning for quantum many-body problems](https://mlsanity.com/paper/2106.12627/) (2021)
- [On barren plateaus and cost function locality in variational quantum algorithms](https://mlsanity.com/paper/2011.10530/) (2020)
- [QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning](https://mlsanity.com/paper/2202.13239/) (2022)
- [A Review of Object Detection Models based on Convolutional Neural Network](https://mlsanity.com/paper/1905.01614/) (2019)
- [Knowledge Distillation Beyond Model Compression](https://mlsanity.com/paper/2007.01922/) (2020)
- [Avoiding Barren Plateaus with Classical Deep Neural Networks](https://mlsanity.com/paper/2205.13418/) (2022)
- [Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2007.06144/) (2020)
- [Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2109.09033/) (2021)
- [CAA-Net: Conditional Atrous CNNs with Attention for Explainable Device-robust Acoustic Scene Classification](https://mlsanity.com/paper/2011.09299/) (2020)
- [QFCNN: Quantum Fourier Convolutional Neural Network](https://mlsanity.com/paper/2106.10421/) (2021)

## Built on by (most cited first, 6 of 6)

- [RMT-PPAD: Real-time Multi-task Learning for Panoptic Perception in Autonomous Driving](https://mlsanity.com/paper/2508.06529/) (2025)
- [CoDS: Enhancing Collaborative Perception in Heterogeneous Scenarios via Domain Separation](https://mlsanity.com/paper/2510.13432/) (2025)
- [Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation](https://mlsanity.com/paper/2410.03174/) (2024)
- [Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection](https://mlsanity.com/paper/2609.14025/) (2026)
- [vFedProtoQNAS: Prototype-Guided Personalized Quantum Neural Architecture Search for Virtual Federated Learning](https://mlsanity.com/paper/2610.01718/) (2026)
- [A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading](https://mlsanity.com/paper/2610.17836/) (2026)

## Abstract (arXiv)

Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, the performances and computation speeds of object detection in autonomous driving have been significantly improved. However, due to the exponentially rapid growth in the complexity and scale of data used in object detection, there are limitations in terms of computation speeds while conducting object detection solely with classical computing. Motivated by this, quantum convolution-based object detection (QCOD) is proposed to adopt quantum computing to perform object detection at high speed. The QCOD utilizes our proposed fast quantum convolution that uploads input channel information and re-constructs output channels for achieving reduced computational complexity and thus improving performances. Lastly, the extensive experiments with KITTI autonomous driving object detection dataset verify that the proposed fast quantum convolution and QCOD are successfully operated in real object detection applications.