Experimental quantum homodyne tomography via machine learning
This provides a more efficient way to characterize quantum resources for communications and computing, though it is incremental as it builds on existing tomography methods.
The authors tackled the problem of characterizing quantum states in large systems by developing a machine learning method using restricted Boltzmann machines for optical homodyne tomography, achieving full state estimation with less experimental data than state-of-the-art techniques due to reduced overfitting.
Complete characterization of states and processes that occur within quantum devices is crucial for understanding and testing their potential to outperform classical technologies for communications and computing. However, solving this task with current state-of-the-art techniques becomes unwieldy for large and complex quantum systems. Here we realize and experimentally demonstrate a method for complete characterization of a quantum harmonic oscillator based on an artificial neural network known as the restricted Boltzmann machine. We apply the method to optical homodyne tomography and show it to allow full estimation of quantum states based on a smaller amount of experimental data compared to state-of-the-art methods. We link this advantage to reduced overfitting. Although our experiment is in the optical domain, our method provides a way of exploring quantum resources in a broad class of large-scale physical systems, such as superconducting circuits, atomic and molecular ensembles, and optomechanical systems.