Learning agent-based approach to the characterization of open quantum systems
Lorenzo Fioroni, Ivan Rojkov, Florentin Reiter

TL;DR
This paper introduces the open Quantum Model Learning Agent (oQMLA), a framework that characterizes both coherent and incoherent dynamics of open quantum systems, improving understanding and mitigation of noise in quantum devices.
Contribution
The paper develops oQMLA, an extension of QMLA that accounts for Markovian noise via Liouvillian formalism, enabling simultaneous learning of Hamiltonian and jump operators.
Findings
Validated robustness in simulated scenarios
Demonstrated ability to characterize systems with local operations
Showcased practical interfacing with superconducting quantum computer
Abstract
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 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…
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