Bayesian homodyne and heterodyne tomography

Area of Science:

• Quantum Information Science
• Quantum Optics
• Quantum Computing

Background:

• Continuous-variable (CV) photonic states are vital for quantum information science.
• Efficient characterization is crucial for advancing CV quantum technologies.
• Bayesian inference offers advantages but hasn't been applied to arbitrary CV state tomography.

Purpose of the Study:

• To introduce a complete Bayesian quantum state tomography workflow.
• To enable inference of generic CV states without assuming Gaussianity.
• To provide a method for characterizing complex quantum states.

Main Methods:

• Developed a Bayesian quantum state tomography workflow.
• Applied the workflow to data from homodyne and heterodyne detection.
• Inferred generic CV states, including coherent, thermal, and cat states.

Main Results:

• Demonstrated a complete Bayesian quantum state tomography workflow.
• Successfully inferred arbitrary CV states without assuming Gaussianity.
• Achieved excellent agreement between Bayesian estimates and theoretical predictions for experimental data.

Conclusions:

• The developed Bayesian workflow is suitable for characterizing generic CV states.
• This method is essential for the maturation of CV quantum technology.
• The approach lays the groundwork for complex CV quantum state estimation in quantum communications and sensors.