New Ranking Loss Boosts Quantum Architecture Search Performance
Researchers at Foshan University introduced a ranking loss function for quantum architecture search that improves the selection of quantum circuits by better aligning with noisy device performance.
The ranking loss could enable QAS to produce circuits with higher fidelity on current noisy quantum hardware, accelerating the deployment of quantum machine learning models.