Markidis Stefano
KTH Royal Institute of Technology, 114 28 Stockholm, Sweden.
Entropy (Basel). 2023 Apr 20;25(4):694. doi: 10.3390/e25040694.
Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces make it possible to explore and investigate the design and development of quantum computing techniques for Machine Learning (ML) applications. Among the most recent quantum ML approaches, Quantum Neural Networks (QNN) emerged as an important tool for data analysis. With the QNN advent, higher-level programming interfaces for QNN have been developed. In this paper, we survey the current state-of-the-art high-level programming approaches for QNN development. We discuss target architectures, critical QNN algorithmic components, such as the hybrid workflow of Quantum Annealers and Parametrized Quantum Circuits, QNN architectures, optimizers, gradient calculations, and applications. Finally, we overview the existing programming QNN frameworks, their software architecture, and associated quantum simulators.
噪声中等规模量子（NISQ）系统及相关编程接口使得探索和研究用于机器学习（ML）应用的量子计算技术的设计与开发成为可能。在最新的量子机器学习方法中，量子神经网络（QNN）成为数据分析的重要工具。随着QNN的出现，已开发出用于QNN的更高级编程接口。在本文中，我们综述了当前用于QNN开发的最先进高级编程方法。我们讨论了目标架构、关键的QNN算法组件，如量子退火器和参数化量子电路的混合工作流程、QNN架构、优化器、梯度计算及应用。最后，我们概述了现有的QNN编程框架、它们的软件架构及相关量子模拟器。
