[2511.09204] Resource-Efficient Variational Quantum Classifier

Quantum Physics arXiv:2511.09204 (quant-ph) [Submitted on 12 Nov 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:Resource-Efficient Variational Quantum Classifier Authors:Petr Ptáček, Paulina Lewandowska, Ryszard Kukulski View a PDF of the paper titled Resource-Efficient Variational Quantum Classifier, by Petr Pt\'a\v{c}ek and 2 other authors View PDF Abstract: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. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2511.09204 [quant-ph]   (or arXiv:2511.09204v2 [quant-ph] for this version)   https:...