HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910902477914112 |
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| author | Zhao, Ren-Xin Tong, Xinze Wang, Shi |
| author_facet | Zhao, Ren-Xin Tong, Xinze Wang, Shi |
| contents | Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifier (HQCC) is proposed. It opens a practical way to advance QML in the Noisy Intermediate-Scale Quantum (NISQ) era by adaptively optimizing the PQC through a Long Short-Term Memory (LSTM) driven dynamic circuit generator, utilizing a local quantum filter for scalable feature extraction, and exploiting architectural plasticity to balance the entanglement depth and noise robustness. We realize the HQCC on the TensorCircuit platform and run simulations on the MNIST and Fashion MNIST datasets, achieving up to 97.12\% accuracy on MNIST and outperforming several alternative methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure Zhao, Ren-Xin Tong, Xinze Wang, Shi Quantum Physics Machine Learning Parameterized Quantum Circuits (PQCs) with fixed structures severely degrade the performance of Quantum Machine Learning (QML). To address this, a Hybrid Quantum-Classical Classifier (HQCC) is proposed. It opens a practical way to advance QML in the Noisy Intermediate-Scale Quantum (NISQ) era by adaptively optimizing the PQC through a Long Short-Term Memory (LSTM) driven dynamic circuit generator, utilizing a local quantum filter for scalable feature extraction, and exploiting architectural plasticity to balance the entanglement depth and noise robustness. We realize the HQCC on the TensorCircuit platform and run simulations on the MNIST and Fashion MNIST datasets, achieving up to 97.12\% accuracy on MNIST and outperforming several alternative methods. |
| title | HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2504.02167 |