Learning the dynamics of Markovian open quantum systems from experimental data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914032793944064 |
|---|---|
| author | Wallace, Stewart Altmann, Yoann Gerardot, Brian D. Gauger, Erik M. Bonato, Cristian |
| author_facet | Wallace, Stewart Altmann, Yoann Gerardot, Brian D. Gauger, Erik M. Bonato, Cristian |
| contents | We present a Bayesian algorithm to identify generators of open quantum system dynamics, described by a Lindblad master equation, that are compatible with measured experimental data. The algorithm, based on a Markov Chain Monte Carlo approach, assumes the energy levels of the system are known and outputs a ranked list of interpretable master equation models that produce predicted measurement traces that closely match experimental data. We benchmark our algorithm on quantum optics experiments performed on single and pairs of quantum emitters. The latter case opens the possibility of cooperative emission effects and additional complexity due to the possible interplay between photon and phonon influences on the dynamics. Our algorithm retrieves various minimal models that are consistent with the experimental data, and which can provide a closer fit to measured data than previously suggested and physically expected approximate models. Our results represent an important step towards automated systems characterisation with an approach that is capable of working with diverse and tomographically incomplete input data. This may help with the development of theoretical models for unknown quantum systems as well as providing scientists with alternative interpretations of the data that they might not have originally envisioned and enabling them to challenge their original hypotheses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning the dynamics of Markovian open quantum systems from experimental data Wallace, Stewart Altmann, Yoann Gerardot, Brian D. Gauger, Erik M. Bonato, Cristian Quantum Physics Mesoscale and Nanoscale Physics Computational Physics Data Analysis, Statistics and Probability Optics We present a Bayesian algorithm to identify generators of open quantum system dynamics, described by a Lindblad master equation, that are compatible with measured experimental data. The algorithm, based on a Markov Chain Monte Carlo approach, assumes the energy levels of the system are known and outputs a ranked list of interpretable master equation models that produce predicted measurement traces that closely match experimental data. We benchmark our algorithm on quantum optics experiments performed on single and pairs of quantum emitters. The latter case opens the possibility of cooperative emission effects and additional complexity due to the possible interplay between photon and phonon influences on the dynamics. Our algorithm retrieves various minimal models that are consistent with the experimental data, and which can provide a closer fit to measured data than previously suggested and physically expected approximate models. Our results represent an important step towards automated systems characterisation with an approach that is capable of working with diverse and tomographically incomplete input data. This may help with the development of theoretical models for unknown quantum systems as well as providing scientists with alternative interpretations of the data that they might not have originally envisioned and enabling them to challenge their original hypotheses. |
| title | Learning the dynamics of Markovian open quantum systems from experimental data |
| topic | Quantum Physics Mesoscale and Nanoscale Physics Computational Physics Data Analysis, Statistics and Probability Optics |
| url | https://arxiv.org/abs/2410.17942 |