论文标题
投影有价值的基于措施的量子机学习用于多类分类
Projection Valued Measure-based Quantum Machine Learning for Multi-Class Classification
论文作者
论文摘要
近年来，量子机器学习（QML）已积极用于各种任务，例如分类，增强学习和对抗性学习。但是，由于输入和输出上的可伸缩性问题，这些QML研究无法执行复杂的任务，这是QML中最大的障碍。因此，本文的目的是克服可伸缩性问题。在这一挑战中，我们专注于投影值评估（PVM），该测量方法利用了量子统计力学中概率幅度的性质。通过利用PVM，输出尺寸从$ Q $（即Qubits的数量）扩展到$ 2^Q $。我们提出了一个新型的QML框架，该框架利用PVM进行多类分类。假设使用不超过6个量子位，我们的框架已被证明超过了具有各种数据集的最先进的方法（SOTA）方法。此外，我们的基于PVM的QML比SOTA框架的性能优于$ 42.2 \％$。
In recent years, quantum machine learning (QML) has been actively used for various tasks, e.g., classification, reinforcement learning, and adversarial learning. However, these QML studies are unable to carry out complex tasks due to scalability issues on input and output which is currently the biggest hurdle in QML. Therefore, the purpose of this paper is to overcome the problem of scalability. Motivated by this challenge, we focus on projection-valued measurements (PVM) which utilize the nature of probability amplitude in quantum statistical mechanics. By leveraging PVM, the output dimension is expanded from $q$, which is the number of qubits, to $2^q$. We propose a novel QML framework that utilizes PVM for multi-class classification. Our framework is proven to outperform the state-of-the-art (SOTA) methodologies with various datasets, assuming no more than 6 qubits are used. Furthermore, our PVM-based QML shows about $42.2\%$ better performance than the SOTA framework.
