In a world with high-capacity and low-latency networks, the ability to efficiently handle huge volumes of data is critical to preventing service disruptions. As demands for communication services have exploded, the complexity of diagnostic schemes have similarly increased, prompting research into approaches using classical and quantum machine learning. This study, conducted by SoftBank Group Corp., demonstrates a novel approach to quantum kernel learning for fault diagnosis using Q-CTRL’s Fire Opal error suppression to enable practical performance on IBM Quantum hardware.

Rowen Wu,Product Manager, Q-CTRL