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Resource-efficient variational quantum classifier
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DOI：10.1007/s42484-026-00439-9.png)
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Abstract
En 中文
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.
Keywords:
Quantum machine learning
Variational quantum classifier
Binary classification
Variational circuit
Unambiguous quantum classifier
Resource-efficient
Journal
Q
IF:
4.4
Papers:
436
Citations:
796