Learning Volterra Kernels for Non-Markovian Open Quantum Systems

• URL: http://arxiv.org/abs/2601.09075v1
• Date: Wed, 14 Jan 2026 02:09:49 GMT
• Title: Learning Volterra Kernels for Non-Markovian Open Quantum Systems
• Authors: Jimmie Adriazola, Katarzyna Roszak,
• Abstract summary: We develop a data-driven framework for identifying non-Markovian dynamical equations of motion for open quantum systems.<n>We establish well-posedness of the learnin
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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