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.

本研究提出了一种基于 PVM 的新型量子机器学习框架，通过利用概率幅度的特性，使输出维度从 q（量子位数）扩展到 2^q，可以用于多类分类，并证明其在各种数据集上的表现均优于当前的 SOTA 方法，同时在不超过 6 个量子位的情况下，其性能比 SOTA 框架高出约 42.2%。