Resource-Efficient Variational Quantum Classifier

TL;DR

This paper presents a resource-efficient quantum classifier that improves accuracy and noise robustness while significantly reducing circuit evaluations, demonstrated on a breast cancer dataset.

Contribution

The paper introduces an unambiguous quantum classifier that enhances performance and noise resilience with fewer circuit evaluations, supported by theoretical analysis.

Findings

Achieved 90% accuracy on breast cancer data, 6.9% above baseline.

Reduced circuit evaluations by a factor of eight per prediction.

Maintained improved accuracy and efficiency under noisy conditions.

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.