Papers › Selective and efficient quantum state tomography for multi-qubit systems
Selective and efficient quantum state tomography for multi-qubit systems
Aniket Patel, Akshay Gaikwad, Tangyou Huang, Anton Frisk Kockum, Tahereh Abad
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Quantum state tomography (QST) is a crucial tool for characterizing quantum states. However, QST becomes impractical for reconstructing multi-qubit density matrices since data sets and computational costs grow exponentially with qubit number. In this Letter, we introduce selective and efficient QST (SEEQST), an approach for efficiently estimating multiple selected elements of an arbitrary N-qubit density matrix. We show that any N-qubit density matrix can be partitioned into 2ᴺ subsets, each containing 2ᴺ elements. With SEEQST, any such subset can be accurately estimated from just two experiments with only single-qubit measurements. The complexity for estimating any subset remains constant regardless of Hilbert-space dimension, so SEEQST can find the full density matrix using 2ᴺ⁺¹ - 1 experiments, where standard methods would use 3ᴺ experiments. We provide a circuit decomposition for the SEEQST experiments, demonstrating that their maximum circuit depth scales logarithmically with N assuming all-to-all connectivity. The Python code for SEEQST is publicly available at \href{https://github.com/aniket-ae/SEEQST}{github.com/aniket-ae/SEEQST}.
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