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.

翻译：我们提出了一种基于汉明距离测量并结合经典后处理的无歧义量子分类器。该方法通过更有效地利用拟设表达性来提升分类性能，同时显著减少电路评估次数。此外，该方法展现出更强的噪声鲁棒性，这对近期的量子器件至关重要。我们在乳腺癌分类数据集上对所提方法进行了评估。该无歧义分类器实现了平均90%的准确率，相比基线提升了6.9个百分点，且每次预测所需的电路执行次数减少了八倍。在存在噪声的情况下，性能提升降至约3.1个百分点，但执行成本降低幅度保持不变。我们通过理论证据支持了该方法在实际性能中的表现，从而验证了实验结果的可靠性。