Efficient Quantum Mixed-State Tomography with Unsupervised Tensor Network Machine Learning

Quantum state tomography (QST) is plagued by the "curse of dimensionality" due to the exponentially-scaled complexity in measurement and data post-processing. Efficient QST schemes for large-scale mixed states are currently missing. In this work, we propose an efficient and robust mixed-state tomography scheme based on the locally purified state ansatz. We demonstrate the efficiency and robustness of our scheme on various randomly initiated states with different purities. High tomography fidelity is achieved with much smaller numbers of positive-operator-valued measurement (POVM) bases than the conventional least-square (LS) method. On the superconducting quantum experimental circuit [Phys. Rev. Lett. 119, 180511 (2017)], our scheme accurately reconstructs the Greenberger-Horne-Zeilinger (GHZ) state and exhibits robustness to experimental noises. Specifically, we achieve the fidelity F simeq 0.92 for the 10-qubit GHZ state with just N_m = 500 POVM bases, which far outperforms the fidelity F simeq 0.85 by the LS method using the full N_m = 3¹⁰ = 59049 bases. Our work reveals the prospects of applying tensor network state ansatz and the machine learning approaches for efficient QST of many-body states.