We revisit the application of neural networks 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.
CITATION STYLE
Koutný, D., Motka, L., Hradil, Z., Řeháček, J., & Sánchez-Soto, L. L. (2022). Neural-network quantum state tomography. Physical Review A, 106(1). https://doi.org/10.1103/PhysRevA.106.012409
Mendeley helps you to discover research relevant for your work.