一句话：提出混合CNN-量子框架，用幅度编码启发技术与新激活函数，仅需 ⌈log2(类别数)⌉ 个读出量子比特完成多类分类。
混合经典-量子机器学习在图像分类中具有潜力，但现有方法通常需要大量量子比特，成为实际应用瓶颈：要么因量子电路设计复杂而局限于二分类，要么依赖 CNN 特征提取器与量子层接口，所需量子比特数常等于提取特征数。本文提出一种新的混合 CNN-量子框架，核心是受幅度编码启发的技术以及新的激活函数，使最终分类仅需 ⌈log2(类别数)⌉ 个读出量子比特。作者在 MNIST、Fashion-MNIST 和 KMNIST 上验证，分别达到 0.9697、0.8515 和 0.9258 的准确率，且仅使用 8 个量子比特；该结果匹配或超过此前使用超过 1500 个量子比特的方法，表明在显著降低量子硬件需求的同时仍具竞争力。
Hybrid classical-quantum machine learning is a promising approach for image classification, but current methods often require too many qubits (quantum resources), creating a bottleneck for practical use. Current methodologies either constrain classification to basic binary decisions due to intricate quantum circuit design or depend on a Convolutional Neural Network (CNN) feature extractor that interfaces with a quantum layer necessitating a substantial quantity of qubits, frequently equivalent to the number of extracted features. To solve this, we propose a new Hybrid CNN-Quantum framework that dramatically reduces the required quantum resources. Our key innovation is an amplitude-encoding-inspired technique and a new activation function that together allow us to infer the final classification using only ⌈log2 (number of classes)⌉ readout qubits. We validated our framework on MNIST, Fashion-MNIST, and KMNIST, achieving accuracies of 0.9697, 0.8515, and 0.9258, respectively, using just 8 qubits. This matches or surpasses prior results that used over 1500 qubits, highlighting our method's competitive accuracy with drastically reduced quantum hardware requirements.