Neural-network quantum state tomography

• URL: http://arxiv.org/abs/2206.06736v1
• Date: Tue, 14 Jun 2022 10:37:54 GMT
• Title: Neural-network quantum state tomography
• Authors: D. Koutny, L. Motka, Z. Hradil, J. Rehacek and L. L. Sanchez-Soto
• Abstract summary: We revisit the application of neural networks techniques to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We revisit the application of neural networks techniques to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.

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