Scaling up current-era quantum processors would require a better understanding of the underlying noise processes affecting the device.  Although protocols such as randomized benchmarking (RB) can help identify non-Markovian correlations in the device, they are often used solely to obtain a single practical fidelity metric.  We present a tomography approach for characterizing non-Markovian quantum dynamics through simple RB experiments.  Our method focuses on learning the system-environment interaction by searching the unitary manifold, solely guided by RB data.  Through our approach, we learn about the system-environment interaction in the presence of both Markovian and non-Markovian environments. We accurately estimate RB outputs for sequence lengths up to 5 times longer than the utilized data, with reconstruction fidelities exceeding 99% and mean squared errors below 10^{-6}.  As an application of our work, we quantify leakage rates in exchange-only semiconductor qubits, a task crucial to enabling many quantum error-correcting codes.  Our work provides a readily deployable, scalable framework for quantum system characterization, offering new capabilities for quantum error characterization and mitigation in realistic quantum hardware.

Pizza and drinks will be served after the seminar in ATL 2117.