一句话：面向光子量子计算机设计并实现噪声感知的量子机器学习分类器 ProxiML。
量子机器学习在分类任务上展现出潜力，但在光子量子计算平台上尚未得到系统探索。本文设计并实现了 ProxiML——一种面向光子量子计算设备的新型量子机器学习分类器，其中包含多项噪声感知设计元素，以支持有效的模型训练与推理。作者在 Xanadu 的 X8 光子真机上进行了大量评估，在具有挑战性的四类分类任务上取得了超过 90% 的准确率，并与先前在其他量子平台上报告的机器学习分类器准确率具有竞争力，揭示了 X8 机器此前未被探索的潜力。
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.
@inproceedings{RanjanPSGT24,
title = {ProxiML: Building Machine Learning Classifiers for Photonic Quantum Computing},
author = {Aditya Ranjan and Tirthak Patel and Daniel Silver and Harshitta Gandhi and Devesh Tiwari},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620666.3651367}
}