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.
@article{arxiv.2511.09204,
title = {Resource-Efficient Variational Quantum Classifier},
author = {Petr Ptáček and Paulina Lewandowska and Ryszard Kukulski},
journal= {arXiv preprint arXiv:2511.09204},
year = {2026}
}
Comments
13 pages, 7 figures, 1 table; typos corrected, new references added, modification of model M3, new result box plots for all models, theoretical results adjusted, abstract and conclusion modified