Brief

Researchers have developed a new variational quantum classifier that improves classification performance by using Hamming distance measurements and classical post-processing. This method requires significantly fewer circuit evaluations and demonstrates enhanced robustness to noise, making it suitable for near-term quantum devices. In experiments on a breast cancer dataset, the classifier achieved an average accuracy of 90%, an improvement of 6.9 percentage points over the baseline, while using eight times fewer circuit executions per prediction. AI