Direct parameter estimations from machine-learning enhanced quantum state tomography

TL;DR

This paper introduces a machine-learning based quantum state tomography method that directly estimates parameters, avoiding large Hilbert space issues and providing high-precision feature extraction for quantum states.

Contribution

Develops a lightweight supervised characteristic model for quantum state tomography that directly estimates parameters from experimental data, improving efficiency and precision.

Findings

The characteristic model accurately predicts quantum noise squeezed state parameters.

Both models agree with covariance method results in experimental data.

The approach is useful for quantum metrology and quantum information applications.

Abstract

With the capability to find the best fit to arbitrarily complicated data patterns, 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 large Hilbert space, but keep feature extractions with high precision. 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 give agreement to 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, including advanced gravitational wave detectors, quantum metrology, macroscopic quantum state generation, and quantum information process.