动态优化量子电路结构，提升噪声环境下的分类准确率
HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure
- 用LSTM驱动的动态电路生成器自适应调整量子线路
- 在MNIST上达97.12%准确率，优于多个基准方法
- 适合研究量子机器学习在真实硬件上的落地应用
固定结构的参数化量子线路（PQC）严重限制了量子机器学习（QML）性能。为此，本文提出一种混合量子-经典分类器（HQCC），通过长短期记忆网络（LSTM）驱动的动态电路生成器自适应优化PQC，结合局部量子滤波实现可扩展特征提取，并利用架构可塑性平衡纠缠深度与抗噪能力。我们在TensorCircuit平台上实现了HQCC，在MNIST和Fashion MNIST数据集上进行模拟，结果表明其在MNIST上最高达到97.12%准确率，优于多种对比方法。
原文摘要 · Abstract (English)
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.
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