&bulet; Physics 17, p. 41
Measuring the photon’s angular momentum after passing through optical instruments teaches an algorithm to reconstruct the properties of the photon’s initial quantum state.
In quantum information processing, it is crucial to be able to extract features from an experimental dataset while lacking direct knowledge of the system that generated it. A promising approach involves quantum deep learning machines, which rely on a training dataset to learn how to infer the quantum properties of a system based on measurements from uncalibrated devices. Alessia Suprano of Rome’s Sapienza University and her colleagues now apply this framework to reconstruct the initial polarization state of a photon based on measurements of its final orbital angular momentum as it passes through a series of optical instruments. Happens after [1]. Their experiment uses a relatively small data set to achieve robust performance, and the reconstruction avoids the need for detailed knowledge of the experimental platform.
In their experiment, the researchers sent photons through a device that placed them in a random initial polarization state. Next, each photon passed through a series of instruments that modified this initial state by randomly manipulating the photon’s orbital angular momentum. Finally, an instrument measured the orbital angular momentum of the output photon, and the result was fed to a computer for processing. By sampling 300 such photons, a machine learning model was trained to reconstruct their initial polarization states.
The researchers say their machine learning model offers an agile and resource-efficient approach, performing 5 to 10 times better than alternative methods for extracting quantum properties from experimental measurements. They also say the result highlights the potential of photonic platforms for important quantum information processing tasks.
– Rachel Berkowitz
Rachel Berkowitz is a corresponding editor. Physics Magazine Based in Vancouver, Canada.
references
- A. Soprano ET.“Reconstruction of experimental properties in a photonic quantum extreme learning machine,” Physics Rev. Lett. 132160802 (2024).