QSTToolkit: A Python Library for Deep Learning Powered Quantum State Tomography
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909541439897600 |
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| author | FitzGerald, George Yeadon, Will |
| author_facet | FitzGerald, George Yeadon, Will |
| contents | We introduce QSTToolkit, a Python library for performing quantum state tomography (QST) on optical quantum state measurement data. The toolkit integrates traditional Maximum Likelihood Estimation (MLE) with deep learning-based techniques to reconstruct quantum states. It includes comprehensive noise models to simulate both intrinsic state noise and measurement imperfections, enabling the realistic recreation of experimental data. QSTToolkit bridges TensorFlow, a leading deep learning framework, with QuTiP, a widely used quantum physics toolbox for Python. This paper describes the library's features, including its data generation capabilities and the various QST methods implemented. QSTToolkit is available at https://pypi.org/project/qsttoolkit/, with full documentation at https://qsttoolkit.readthedocs.io/en/latest/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14422 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QSTToolkit: A Python Library for Deep Learning Powered Quantum State Tomography FitzGerald, George Yeadon, Will Quantum Physics We introduce QSTToolkit, a Python library for performing quantum state tomography (QST) on optical quantum state measurement data. The toolkit integrates traditional Maximum Likelihood Estimation (MLE) with deep learning-based techniques to reconstruct quantum states. It includes comprehensive noise models to simulate both intrinsic state noise and measurement imperfections, enabling the realistic recreation of experimental data. QSTToolkit bridges TensorFlow, a leading deep learning framework, with QuTiP, a widely used quantum physics toolbox for Python. This paper describes the library's features, including its data generation capabilities and the various QST methods implemented. QSTToolkit is available at https://pypi.org/project/qsttoolkit/, with full documentation at https://qsttoolkit.readthedocs.io/en/latest/. |
| title | QSTToolkit: A Python Library for Deep Learning Powered Quantum State Tomography |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2503.14422 |