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Open Quantum Systems Decoherence
Quantum Machine Learning
Learning Volterra Kernels for Non-Markovian Open Quantum Systems
arXiv
Authors: Jimmie Adriazola, Katarzyna Roszak
Year
2026
Paper ID
3859
Status
Preprint
Abstract Read
~2 min
Abstract Words
79
Citations
N/A
Abstract
We develop a data-driven framework for identifying non-Markovian dynamical equations of motion for open quantum systems. Starting from the Nakajima--Zwanzig formalism, we vectorize the reduced density matrix into a four-dimensional state vector and cast the dynamics as a Volterra integro-differential equation with an operator-valued memory kernel. The learning task is then formulated as a constrained optimization problem over the admissible operator space, where correlation functions are approximated by rational functions using Padé approximants. We establish well-posedness of the learnin
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- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- We develop a data-driven framework for identifying non-Markovian dynamical equations of motion for open quantum systems.
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