Quantum feature-map learning with reduced resource overhead
Researchers have developed a new algorithm called Q-FLAIR to reduce the computational resources needed for quantum machine learning feature maps. This method shifts significant workloads to classical computers, enabling the training of complex quantum models with fewer evaluations. Q-FLAIR has demonstrated state-of-the-art performance on classifiers and achieved over 90% accuracy on the MNIST dataset using a real IBM quantum device in just four hours, a feat previously considered unattainable due to hardware demands. AI
IMPACT Enables more complex quantum machine learning models to be trained on near-term quantum hardware.