Machine learning of quantum channels on NISQ devices
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_ | 1866913577012559872 |
|---|---|
| author | Cemin, Giovanni Cech, Marcel Weiss, Erik Soltan, Stanislaw Braun, Daniel Lesanovsky, Igor Carollo, Federico |
| author_facet | Cemin, Giovanni Cech, Marcel Weiss, Erik Soltan, Stanislaw Braun, Daniel Lesanovsky, Igor Carollo, Federico |
| contents | World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channels, are implemented on these devices. To systematically infer those, we propose a neural-network algorithm approximating generic discrete-time dynamics through the repeated action of an effective quantum channel. We test our approach considering time-periodic Lindblad dynamics as well as non-unitary subsystem dynamics in many-body unitary circuits. Moreover, we exploit it to investigate cross-talk effects on the ibmq_ehningen quantum processor, which showcases our method as a practically applicable tool for inferring quantum channels when the exact nature of the underlying dynamics on the physical device is not known a priori. While the present approach is tailored for learning Markovian dynamics, we discuss how it can be adapted to also capture generic non-Markovian discrete-time evolutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_12598 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning of quantum channels on NISQ devices Cemin, Giovanni Cech, Marcel Weiss, Erik Soltan, Stanislaw Braun, Daniel Lesanovsky, Igor Carollo, Federico Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics World-wide efforts aim at the realization of advanced quantum simulators and processors. However, despite the development of intricate hardware and pulse control systems, it may still not be generally known which effective quantum dynamics, or channels, are implemented on these devices. To systematically infer those, we propose a neural-network algorithm approximating generic discrete-time dynamics through the repeated action of an effective quantum channel. We test our approach considering time-periodic Lindblad dynamics as well as non-unitary subsystem dynamics in many-body unitary circuits. Moreover, we exploit it to investigate cross-talk effects on the ibmq_ehningen quantum processor, which showcases our method as a practically applicable tool for inferring quantum channels when the exact nature of the underlying dynamics on the physical device is not known a priori. While the present approach is tailored for learning Markovian dynamics, we discuss how it can be adapted to also capture generic non-Markovian discrete-time evolutions. |
| title | Machine learning of quantum channels on NISQ devices |
| topic | Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2405.12598 |