Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2022
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914966715498496 |
|---|---|
| author | Wang, Quanlong Yeung, Richie Koch, Mark |
| author_facet | Wang, Quanlong Yeung, Richie Koch, Mark |
| contents | ZX-calculus has proved to be a useful tool for quantum technology with a wide range of successful applications. Most of these applications are of an algebraic nature. However, other tasks that involve differentiation and integration remain unreachable with current ZX techniques. Here we elevate ZX to an analytical perspective by realising differentiation and integration entirely within the framework of ZX-calculus. We explicitly illustrate the new analytic framework of ZX-calculus by applying it in context of quantum machine learning for the analysis of barren plateaus. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_13250 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning Wang, Quanlong Yeung, Richie Koch, Mark Quantum Physics Machine Learning ZX-calculus has proved to be a useful tool for quantum technology with a wide range of successful applications. Most of these applications are of an algebraic nature. However, other tasks that involve differentiation and integration remain unreachable with current ZX techniques. Here we elevate ZX to an analytical perspective by realising differentiation and integration entirely within the framework of ZX-calculus. We explicitly illustrate the new analytic framework of ZX-calculus by applying it in context of quantum machine learning for the analysis of barren plateaus. |
| title | Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2201.13250 |