Generative Quantum Machine Learning

Christa Zoufal
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引用次数: 5

Abstract

The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical resources, generative quantum machine learning can also employ quantum resources - such as parameterized quantum channels and quantum operators - to learn and sample from the probability model of interest. Applications of generative (quantum) models are multifaceted. The trained model can generate new samples that are compatible with the given data and extend the data set. Additionally, learning a model for the generation process of a data set may provide interesting information about the corresponding properties. With the help of quantum resources, the respective generative models have access to functions that are difficult to evaluate with a classical computer and may improve the performance or lead to new insights. Furthermore, generative quantum machine learning can be applied to efficient, approximate loading of classical data into a quantum state which may help to avoid - potentially exponentially - expensive, exact quantum data loading. The aim of this doctoral thesis is to develop new generative quantum machine learning algorithms, demonstrate their feasibility, and analyze their performance. Additionally, we outline their potential application to efficient, approximate quantum data loading. More specifically, we introduce a quantum generative adversarial network and a quantum Boltzmann machine implementation, both of which can be realized with parameterized quantum circuits. These algorithms are compatible with first-generation quantum hardware and, thus, enable us to study proof of concept implementations not only with numerical quantum simulations but also real quantum hardware available today.
生成量子机器学习
生成式机器学习的目标是对给定数据集的概率分布进行建模。这种概率分布有助于描述数据样本的生成过程。经典生成式机器学习完全基于经典资源,而生成式量子机器学习也可以利用量子资源(如参数化量子通道和量子算子)从感兴趣的概率模型中学习和采样。生成(量子)模型的应用是多方面的。训练后的模型可以生成与给定数据兼容的新样本,并扩展数据集。此外,学习数据集生成过程的模型可能会提供有关相应属性的有趣信息。在量子资源的帮助下,各自的生成模型可以访问难以用经典计算机评估的函数,并可能提高性能或导致新的见解。此外,生成式量子机器学习可以应用于高效、近似地将经典数据加载到量子状态,这可能有助于避免(可能呈指数级)昂贵的精确量子数据加载。本博士论文的目的是开发新的生成式量子机器学习算法,论证其可行性,并分析其性能。此外,我们概述了它们在高效、近似量子数据加载方面的潜在应用。更具体地说,我们介绍了一种量子生成对抗网络和一种量子玻尔兹曼机实现,两者都可以通过参数化量子电路实现。这些算法与第一代量子硬件兼容,因此,我们不仅可以用数值量子模拟,还可以用当今可用的真实量子硬件来研究概念实现的证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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