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arXiv · 2503.14422

QSTToolkit: A Python Library for Deep Learning Powered Quantum State Tomography

George FitzGerald·Will Yeadon
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Abstract

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/.

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George FitzGerald, Will Yeadon. 2025-03-18. QSTToolkit: A Python Library for Deep Learning Powered Quantum State Tomography. https://arxiv.org/abs/2503.14422

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