Online learning of quantum processes

Understand

Among recent insights into learning quantum states, online learning and shadow tomography procedures are notable for their ability to accurately predict expectation values even of adaptively chosen observables.

• In contrast to the state case, quantum process learning tasks with a similarly adaptive nature have received little attention.
• In this work, we investigate online learning tasks for quantum processes.
• Whereas online learning is infeasible for general quantum channels, we show that channels of bounded gate complexity as well as Pauli channels can be online learned in the regret and mistake-bounded models of online learning.