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With the capability to find the best fit to arbitrarily complicated datapatterns, machine-learning (ML) enhanced quantum state tomography (QST) hasdemonstrated its advantages in extracting complete information about thequantum states. Instead of using the reconstruction model in training atruncated density matrix, we develop a high-performance, lightweight, andeasy-to-install supervised characteristic model by generating the targetparameters directly. Such a characteristic model-based ML-QST can avoid theproblem of dealing with large Hilbert space, but keep feature extractions withhigh precision. With the experimentally measured data generated from thebalanced homodyne detectors, we compare the degradation information aboutquantum noise squeezed states predicted by the reconstruction andcharacteristic models, both give agreement to the empirically fitting curvesobtained from the covariance method. Such a ML-QST with direct parameterestimations illustrates a crucial diagnostic toolbox for applications withsqueezed states, including advanced gravitational wave detectors, quantummetrology, macroscopic quantum state generation, and quantum informationprocess.

Direct parameter estimations from machine-learning enhanced quantum state tomography