Machine-learning certification of multipartite entanglement for noisy quantum hardware
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929469807132672 |
|---|---|
| author | Fuchs, Andreas J. C. Brunner, Eric Seong, Jiheon Kwon, Hyeokjea Seo, Seungchan Bae, Joonwoo Buchleitner, Andreas Carnio, Edoardo G. |
| author_facet | Fuchs, Andreas J. C. Brunner, Eric Seong, Jiheon Kwon, Hyeokjea Seo, Seungchan Bae, Joonwoo Buchleitner, Andreas Carnio, Edoardo G. |
| contents | Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine-learning certification of multipartite entanglement for noisy quantum hardware Fuchs, Andreas J. C. Brunner, Eric Seong, Jiheon Kwon, Hyeokjea Seo, Seungchan Bae, Joonwoo Buchleitner, Andreas Carnio, Edoardo G. Quantum Physics Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware. |
| title | Machine-learning certification of multipartite entanglement for noisy quantum hardware |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2408.12349 |