  Quantum state tomography, a process that reconstructs a quantum state from
measurements on an ensemble of identically prepared copies, plays a crucial
role in benchmarking quantum devices. However, brute-force approaches to
quantum state tomography would become impractical for large systems, as the
required resources scale exponentially with the system size. Here, we explore a
machine learning approach and report an experimental demonstration of
reconstructing quantum states based on neural network generative models with an
array of programmable superconducting transmon qubits. In particular, we
experimentally prepare the Greenberger-Horne-Zeilinger states and random states
up to five qubits and demonstrate that the machine learning approach can
efficiently reconstruct these states with the number of required experimental
samples scaling linearly with system size. Our results experimentally showcase
the intriguing potential for exploiting machine learning techniques in
validating and characterizing complex quantum devices, offering a valuable
guide for the future development of quantum technologies.
