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Learning Topological States from Randomized Measurements Using Variational Tensor-Network Tomography

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¹Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
²Institute of Physics, Ecole Polytechnique Fédéderale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
³Department of Physics, Princeton University, Princeton, New Jersey 08544, USA
⁴Princeton Center for Theoretical Science, Princeton University, Princeton, New Jersey 08544, USA
⁵Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany
⁶IBM Quantum, Almaden Research Center, San Jose, California 95120, USA
⁷IBM Quantum, IBM T.J. Watson Research Center, Yorktown Heights, New York 10598, USA
⁸MIT-IBM Watson AI Lab, Cambridge, Massachusetts 02142, USA

^*Contact author: adateng07@gmail.com

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Articles Citing

Learning Topological States from Randomized Measurements Using Variational Tensor-Network Tomography

Quantum machine learning for state tomography using classical data

Semi-Dirac spin liquids and frustrated quantum magnetism on the trellis lattice

Learning Mixed Quantum States in Large-Scale Experiments

Quantum state readout via overlap-based feature extraction

Fermionic parton theory of Rydberg $${{\mathbb{Z}}}_{2}$$ quantum spin liquids

Learning Topological States from Randomized Measurements Using Variational Tensor-Network Tomography

No-Free-Lunch Theorems for Tensor Network Machine Learning Models

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