With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machine learning could be a solution for the increasing demand of urgent analytics, providing potential theoretical speedups and increased space efficiency. However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics. In this work, we investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum, identifying the main challenges and possible solutions. Furthermore, exploring the data encoding and hyperparameter tuning challenges, we present preliminary results for quantum machine learning analytics on an IoT scenario.

翻译：随着后摩尔时代的到来，科学界面临着应对当前数据密集型机器学习应用需求的挑战，这些应用是分布式计算中紧急分析的基石。量子机器学习可能成为应对日益增长的紧急分析需求的解决方案，提供潜在的理论加速和更高的空间效率。然而，诸如（1）数据从经典域到量子域的编码、（2）超参数调优以及（3）量子硬件集成到分布式计算连续体中的挑战，限制了量子机器学习在紧急分析中的应用。在这项工作中，我们研究了使用边缘计算将量子机器学习集成到分布式计算连续体中的方法，识别了主要挑战和可能的解决方案。此外，针对数据编码和超参数调优的挑战，我们展示了在物联网场景下量子机器学习分析的初步结果。