Abstract

The integration of quantum computing and machine learning has ushered in a transformative era in data analysis, characterized by the development of Quantum Machine Learning (QML). This chapter explores the pivotal role of standardization in evaluation practices within QML research, addressing the challenges posed by inconsistent metrics, diverse benchmarking standards, and reproducibility concerns. Emphasis was placed on the establishment of unified evaluation metrics tailored to quantum algorithms, which can enhance the comparability of results across studies. Additionally, the chapter advocates for the creation of standardized benchmarking frameworks and reproducibility guidelines to bolster the reliability of QML findings. Collaborative initiatives within the research community are encouraged to promote knowledge sharing and best practices. By fostering standardized evaluation practices, this chapter aims to enhance the credibility and impact of QML research, paving the way for innovative advancements in various applications, including finance, healthcare, and artificial intelligence.

Introduction

The advent of quantum computing has initiated a paradigm shift in the field of data analysis, leading to the development of Quantum Machine Learning (QML) [1]. This innovative approach leverages the principles of quantum mechanics to process and analyze data at unprecedented speeds and efficiencies [2,3,4]. By harnessing the unique capabilities of quantum systems, researchers are exploring new algorithms that promise to revolutionize machine learning tasks such as classification, regression, and clustering [5]. The potential for QML to solve complex problems that are intractable for classical computing paradigms has garnered significant attention across various domains, including finance, healthcare, and artificial intelligence [6]. As QML continues to evolve, the establishment of standardized evaluation practices becomes increasingly crucial for measuring the performance and reliability of quantum algorithms [7].

Even though QML is developing quickly, there are still a lot of issues with evaluation procedures in the field. [8]. The lack of consistent metrics and benchmarks has resulted in a fragmented landscape, complicating comparisons between different studies and algorithms [9]. Various research efforts utilize disparate evaluation criteria, which often leads to confusion about the actual performance of quantum models [10]. The absence of standardization hampers the reproducibility of results, a fundamental aspect of scientific research [11]. This lack of clarity not only affects the credibility of QML research but also limits its adoption in practical applications [12]. Addressing these challenges requires a concerted effort to develop standardized evaluation practices that can enhance the reliability and comparability of QML findings [13,14,15].