PhoQuPy: A Python framework for Automation of Quantum Optics experiments

TL;DR

This paper introduces PhoQuPy, a Python framework that automates quantum optics experiments, including photoluminescence scanning, single-photon emitter characterization, and hyperspectral imaging, enhancing efficiency and reproducibility.

Contribution

The paper presents a comprehensive Python-based automation system for quantum optics experiments, integrating hardware control and data processing for various advanced measurements.

Findings

Successful automation of confocal PL scanning and SPE characterization.

Generation of spatially resolved PL maps and temperature-dependent spectra.

Demonstration of automated quantum imaging and hyperspectral measurements.

Abstract

We present the automation of a confocal photoluminescence (PL) scanning system for the identification and characterization of single-photon emitters (SPEs) in quantum materials. The setup excites the sample with a laser and acquires a spectrum at each spatial coordinate in a raster scan pattern. A double-acquisition method is used to remove cosmic ray artifacts by comparing subsequent measurements at the same spatial coordinate. Once identified, the emitter is further characterized via a HBT setup, thereby measuring lifetime as well as second-order autocorrelation g(2) measurements to confirm singlephoton emission. The system integrates Python-based hardware control for motorized stages, spectrometer acquisition, and post-processing, with a migration to a galvo-mirror scanning approach for using it along with a cryostat for low temperature measurements. Our results demonstrate spatially resolved PL maps and temperature-dependent spectra, highlighting the capability of the setup to efficiently benchmark SPE performance. We further went on to perform automation of other experiments such as a Non-Linear Interferometry setup for Quantum Imaging with Undetected Light and a Fourier Transform Imaging Spectroscopy using a common path birefringence Interferometer to obtain hyperspectral images of our samples.