Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.23560 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915751550517248 |
|---|---|
| author | Sofi, Shakir Showkat Vermeylen, Charlotte De Lathauwer, Lieven |
| author_facet | Sofi, Shakir Showkat Vermeylen, Charlotte De Lathauwer, Lieven |
| contents | Quantum state tomography (QST) is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantum devices. However, standard estimation methods become impractical due to the exponential growth of parameters in the state representation. In this work, we address this challenge by parameterizing the state using a low-rank block tensor train decomposition and demonstrate that our approach is both memory- and computationally efficient. This framework applies to a broad class of quantum states that can be well approximated by low-rank decompositions, including pure states, nearly pure states, and ground states of Hamiltonians. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23560 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tensor Train Quantum State Tomography using Compressed Sensing Sofi, Shakir Showkat Vermeylen, Charlotte De Lathauwer, Lieven Quantum Physics Artificial Intelligence Signal Processing Optimization and Control Quantum state tomography (QST) is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evaluating the performance of quantum devices. However, standard estimation methods become impractical due to the exponential growth of parameters in the state representation. In this work, we address this challenge by parameterizing the state using a low-rank block tensor train decomposition and demonstrate that our approach is both memory- and computationally efficient. This framework applies to a broad class of quantum states that can be well approximated by low-rank decompositions, including pure states, nearly pure states, and ground states of Hamiltonians. |
| title | Tensor Train Quantum State Tomography using Compressed Sensing |
| topic | Quantum Physics Artificial Intelligence Signal Processing Optimization and Control |
| url | https://arxiv.org/abs/2506.23560 |