用汉明距离提升量子分类器效率，少算8倍还更抗噪。
Resource-Efficient Variational Quantum Classifier
- 基于汉明距离测量与后处理实现无歧义分类
- 准确率90%比基线高6.9个百分点，仅需1/8计算量
- 适合资源受限的近中期量子设备应用
我们提出一种基于汉明距离测量的无歧义量子分类器，结合经典后处理。该方法通过更高效利用变分电路表达能力，显著减少电路评估次数。在乳腺癌分类数据集上，该分类器平均准确率达90%，相比基线提升6.9个百分点，且每次预测所需电路执行次数减少8倍。在噪声环境下，性能提升降至约3.1个百分点，但计算开销仍保持相同降低。实验结果得到理论支持，验证了该方法在实际场景中的有效性。
原文摘要 · Abstract (English)
We introduce the unambiguous quantum classifier based on Hamming distance measurements combined with classical post-processing. The proposed approach improves classification performance through a more effective use of ansatz expressivity, while requiring significantly fewer circuit evaluations. Moreover, the method demonstrates enhanced robustness to noise, which is crucial for near-term quantum devices. We evaluate the proposed method on a breast cancer classification dataset. The unambiguous classifier achieves an average accuracy of 90%, corresponding to an improvement of 6.9 percentage points over the baseline, while requiring eight times fewer circuit executions per prediction. In the presence of noise, the improvement is reduced to approximately 3.1 percentage points, with the same reduction in execution cost. We substantiate our experimental results with theoretical evidence supporting the practical performance of the approach.
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