ProxiML: Building Machine Learning Classifiers for Photonic Quantum Computing

Quantum machine learning has shown early promise and potential for productivity improvements for machine learning classification tasks, but has not been systematically explored on photonics quantum computing platforms. Therefore, this paper presents the design and implementation of ProxiML - a novel quantum machine learning classifier for photonic quantum computing devices with multiple noise-aware design elements for effective model training and inference. Our extensive evaluation on a photonic device (Xanadu's X8 machine) demonstrates the effectiveness of ProxiML machine learning classifier (over 90% accuracy on a real machine for challenging four-class classification tasks), and competitive classification accuracy compared to prior reported machine learning classifier accuracy on other quantum platforms - revealing the previously unexplored potential of Xanadu's X8 machine.