Error Mitigation in Data Driven Circuit Learning

Mitigating state preparation and measurement (SPAM) errors has been shown to improve the performance of noisy intermediate scale quantum (NISQ) devices. This talk focuses on the incorporation of matrix-based SPAM error mitigation into data-driven circuit learning for parameterized circuits implementing generative modeling tasks. We discuss how the choice of nonlinear optimization, loss function and the structure of the target distributions can affect the computational cost associated with gradient-based training of densely parameterized quantum circuits trained on NISQ hardware accessed via cloud-based queues.