Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography

With the power to find the best fit to arbitrarily complicated symmetry, machine-learning (ML)-enhanced quantum state tomography (QST) has demonstrated its advantages in extracting complete information about the quantum states. Instead of using the reconstruction model in training a truncated density matrix, we develop a high-performance, lightweight, and easy-to-install supervised characteristic model by generating the target parameters directly. Such a characteristic model-based ML-QST can avoid the problem of dealing with a large Hilbert space, but cab keep feature extractions with high precision, capturing the underlying symmetry in data. With the experimentally measured data generated from the balanced homodyne detectors, we compare the degradation information about quantum noise squeezed states predicted by the reconstruction and characteristic models; both are in agreement with the empirically fitting curves obtained from the covariance method. Such a ML-QST with direct parameter estimations illustrates a crucial diagnostic toolbox for applications with squeezed states, from quantum information process, quantum metrology, advanced gravitational wave detectors, to macroscopic quantum state generation.

Automated summary

Machine-learning-enhanced quantum state tomography (QST) can extract complete information about quantum states by finding the best fit to arbitrarily complicated symmetry. By using a characteristic model that directly estimates parameters, ML-QST can avoid dealing with a large Hilbert space while maintaining high precision in feature extraction, making it a crucial diagnostic toolbox for applications with squeezed states.