Learning Volterra Kernels for Non-Markovian Open Quantum Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908763603075072 |
|---|---|
| author | Adriazola, Jimmie Roszak, Katarzyna |
| author_facet | Adriazola, Jimmie Roszak, Katarzyna |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09075 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Volterra Kernels for Non-Markovian Open Quantum Systems Adriazola, Jimmie Roszak, Katarzyna Quantum Physics Optimization and Control 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 |
| title | Learning Volterra Kernels for Non-Markovian Open Quantum Systems |
| topic | Quantum Physics Optimization and Control |
| url | https://arxiv.org/abs/2601.09075 |