ArticleEntropy (Basel, Switzerland)2022
A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A novel diabetic retinopathy detection from fundus images using hybrid quantum convolutional neural network models.Scientific reports · 2026Article
- Benchmarking quantum versions of the kNN algorithm with a metric based on amplitude-encoded features.Scientific reports · 2024Article
- Medical image diagnosis based on adaptive Hybrid Quantum CNN.BMC medical imaging · 2023Article
- A Quantum-Classical Hybrid Solution for Deep Anomaly Detection.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification. The major challenge comes from the limitations of near-term quantum devices on the number of qubits and the size of quantum circuits. In this paper, we propose a hybrid quantum neural network to implement multi-classification of a real-world dataset. We use an average pooling downsampling strategy to reduce the dimensionality of samples, and we design a ladder-like parameterized quantum circuit to disentangle the input states. Besides this, we adopt an all-qubit multi-observable measurement strategy to capture sufficient hidden information from the quantum system. The experimental results show that our algorithm outperforms the classical neural network and performs especially well on different multi-class datasets, which provides some enlightenment for the application of quantum computing to real-world data on near-term quantum processors.
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