Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908515624288256 |
|---|---|
| author | Manna, Sayan R, Mahesh Mohan M |
| author_facet | Manna, Sayan R, Mahesh Mohan M |
| contents | Quantum algorithms operate on quantum states through unitary transformations in high dimensional complex Hilbert space. In this work, we propose a machine learning approach to create the quantum circuit using a single-layer complex-valued neural network. The input and ouput quantum states are provided to the network, which is trained to approximate the output state of a given quantum algorithm. To ensure that the fundamental property of unitarity is preserved throughout the training process, we employ optimization in Stiefel Manifold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold Manna, Sayan R, Mahesh Mohan M Quantum Physics Quantum algorithms operate on quantum states through unitary transformations in high dimensional complex Hilbert space. In this work, we propose a machine learning approach to create the quantum circuit using a single-layer complex-valued neural network. The input and ouput quantum states are provided to the network, which is trained to approximate the output state of a given quantum algorithm. To ensure that the fundamental property of unitarity is preserved throughout the training process, we employ optimization in Stiefel Manifold. |
| title | Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2509.02374 |