论文标题
工程师的量子机学习简介
An Introduction to Quantum Machine Learning for Engineers
论文作者
论文摘要
在当前嘈杂的中间尺度量子（NISQ）时代，量子机学习正在成为基于程序门的量子计算机的主要范式。在量子机学习中，量子电路的门被参数化，并且参数是根据数据和电路输出的测量来通过经典优化调整的。参数化的量子电路（PQC）可以有效地解决组合优化问题，实现概率生成模型并进行推理（分类和回归）。该专着为具有概率和线性代数背景的工程师的观众提供了量子机学习的独立介绍。它首先描述了描述量子操作和测量所必需的必要背景，概念和工具。然后，它涵盖了参数化的量子电路，变异的量子本索，以及无监督和监督的量子机学习公式。
In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.
