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Title: Quantum state tomography with tensor train cross approximation
It has been recently shown that a state generated by a one-dimensional noisy quantum computer is well approximated by a matrix product operator with a finite bond dimension independent of the number of qubits. We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings using a method known as tensor train cross approximation. The method works for reconstructing full rank density matrices and only requires measuring local operators, which are routinely performed in state-of-art experimental quantum platforms. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements. The fidelity of our reconstructed state can be further improved via supervised machine learning, without demanding more experimental data. Scalable tomography is achieved if the full state can be reconstructed from local reductions.  more » « less
Award ID(s):
2112893 1839232
NSF-PAR ID:
10352914
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
ArXivorg
ISSN:
2331-8422
Page Range / eLocation ID:
2207.06397
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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