Learning-agent-based approach to the characterization of open quantum systems

Characterizing quantum processes is crucial for the execution of quantum algorithms on available quantum devices. A powerful framework for this purpose is the Quantum Model Learning Agent (QMLA), which characterizes a given system by learning its Hamiltonian via adaptive generations of informative experiments and their validation against simulated models. Identifying the incoherent noise of a quantum device in addition to its coherent interactions is, however, as essential. Precise knowledge of such imperfections of a quantum device allows one to devise strategies to mitigate detrimental effects, for example, via quantum error correction. We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism. By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system. The added complexity of open systems necessitates advanced algorithmic strategies. Among these, we implement regularization to steer the algorithm toward plausible models and an unbiased metric to evaluate the quality of the results. We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors and its ability to characterize systems using only local operations. Additionally, we develop a scheme to interface oQMLA with a publicly available superconducting quantum computer, showcasing its practical utility. These advancements represent a significant step toward improving the performance of quantum hardware and contribute to the broader goal of advancing quantum technologies and their applications.