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This preprint introduces a resource-efficient quantum classifier that leverages Hamming distance measurements to achieve significant accuracy gains with far fewer circuit executions. The approach demonstrates enhanced noise robustness, making it particularly relevant for near-term quantum hardware applications in classification tasks like medical diagnostics. By combining effective ansatz expressivity with classical post-processing, the method offers a practical pathway toward viable variational algorithms on current devices.
Resource-Efficient Variational Quantum Classifier
arXiv:2511.09204v3 Announce Type: replace-cross 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.
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.
Quantum Physics (quant-ph); Machine Learning (cs.LG)
arXiv:2511.09204 [quant-ph]
(or arXiv:2511.09204v3 [quant-ph] for this version)
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