Multi-Class Quantum Convolutional Neural Networks
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Abstract (arXiv)
Classification is particularly relevant to Information Retrieval, as it is used in various subtasks of the search pipeline. In this work, we propose a quantum convolutional neural network (QCNN) for multi-class classification of classical data. The model is implemented using PennyLane. The optimization process is conducted by minimizing the cross-entropy loss through parameterized quantum circuit optimization. The QCNN is tested on the MNIST dataset with 4, 6, 8 and 10 classes. The results show that with 4 classes, the performance is slightly lower compared to the classical CNN, while with a higher number of classes, the QCNN outperforms the classical neural network.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 19 Apr 2024.
Builds on
9 of 26 references.
References · 9
- 2020The power of quantum neural networkscited by 377
- 2021Generalization in quantum machine learning from few training datacited by 214
- 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
- 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
- 2022Retrieval-Enhanced Machine Learningcited by 46
- 2022Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantagecited by 18
- 2022Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learningcited by 16
- 2023A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answeringcited by 14
- 2023QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifierscited by 11
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.