mlsanityData to 7 Oct 2026

3D Scalable Quantum Convolutional Neural Networks for Point Cloud Data Processing in Classification Applications

arXiv 2210.09728 ↗First posted 18 Oct 2022quant-ph cs.CVcited by 4

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Abstract (arXiv)

With the beginning of the noisy intermediate-scale quantum (NISQ) era, a quantum neural network (QNN) has recently emerged as a solution for several specific problems that classical neural networks cannot solve. Moreover, a quantum convolutional neural network (QCNN) is the quantum-version of CNN because it can process high-dimensional vector inputs in contrast to QNN. However, due to the nature of quantum computing, it is difficult to scale up the QCNN to extract a sufficient number of features due to barren plateaus. Motivated by this, a novel 3D scalable QCNN (sQCNN-3D) is proposed for point cloud data processing in classification applications. Furthermore, reverse fidelity training (RF-Train) is additionally considered on top of sQCNN-3D for diversifying features with a limited number of qubits using the fidelity of quantum computing. Our data-intensive performance evaluation verifies that the proposed algorithm achieves desired performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

22 of 39 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  3. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  4. 2018Barren plateaus in quantum neural network training landscapescited by 624
  5. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  6. 2017Large-scale Point Cloud Semantic Segmentation with Superpoint Graphscited by 384
  7. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  8. 2016CNN-RNN: A Unified Framework for Multi-label Image Classificationcited by 189
  9. 2017Semantic3D.net: A new Large-scale Point Cloud Classification Benchmarkcited by 158
  10. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  11. 2019PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representationcited by 114
  12. 2020Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Reviewcited by 107
10 more
  1. 2020Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applicationscited by 62
  2. 2018A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Drivingcited by 54
  3. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  4. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  5. 2020Universal Approximation Theorem for Equivariant Maps by Group CNNscited by 12
  6. 2022Scalable Quantum Convolutional Neural Networkscited by 5
  7. 2022Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithmscited by 4
  8. 2022Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimizationcited by 3
  9. 2021Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehiclescited by 1
  10. 2022Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applicationscited by 1

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 2.

4 papers.

  1. 2025Quantum Visual Fields with Neural Amplitude Encodingcited by 6
  2. 2022Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classificationcited by 4
  3. 2022Quantum Split Neural Network Learning using Cross-Channel Poolingcited by 4
  4. 2025PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learningcited by 0