• Physics 17, s41
Measuring the angular momentum of a photon after passing through an optical device teaches an algorithm to reconstruct the properties of the photon's initial quantum state.
In quantum information processing, it is critical to be able to extract features from experimental datasets, even when we lack direct knowledge of the systems that generated them. One promising approach is quantum limit learning machines, which rely on training datasets to figure out how to estimate the quantum properties of a system based on measurements from uncalibrated devices. Alessia Suprano and colleagues at Sapienza University of Rome are now applying this framework to reconstruct a photon's initial polarization state based on measurements of its final orbital angular momentum after passing through a series of optical devices. . [1]. Their experiments use a relatively small dataset to achieve robust performance, and the reconstruction eliminates the need for detailed information about the experimental platform.
In their experiments, the researchers sent single photons through a device that put them into random initial polarization states. Each photon then passed through a series of devices that altered this initial state by randomly manipulating the photon's orbital angular momentum. Finally, the instrument measured the orbital angular momentum of the output photon and inputted the results into a computer for processing. By sampling 300 such photons, a machine learning model was trained to reconstruct the initial polarization state.
The researchers say their machine learning model provides an agile and resource-efficient approach that performs five to 10 times better than alternative methods of obtaining quantum properties from experimental measurements. The researchers also say the results highlight the potential of photonic platforms for important quantum information processing tasks.
–Rachel Berkowitz
Rachel Berkowitz is physics magazine Based in Vancouver, Canada.
References
- A. Suprano other.“Reconstruction of experimental properties in photonic quantum limit learning machines” Physics. Pastor Rhett. 132160802 (2024).
