A Structure‐Based Benchmarking Suite for Quantum Learning

ABSTRACT The application of quantum computing to Data Processing has naturally extended into the realm of quantum machine learning. However, while numerous benchmarks exist for evaluating quantum hardware, there remains a scarcity of tailored benchmarks that assess the intrinsic performance of learning models. This gap makes it difficult for end‐users to determine whether their chosen quantum computing system can effectively perform learning tasks. To address this, we propose a novel and systematic suite for establishing quantum learning benchmarks. In this work, we decompose the learning workflow into three core components: Data Representation, Data Processing, and Model Optimization. Furthermore, instead of considering all methods for each component, we selected fundamental or predominantly used methods. The resulting benchmark suite consists of carefully selected benchmarks, including Basis, Angle, and Amplitude Encoding for Data Representation; Quantum Fourier Transform, Quantum Amplitude Amplification, and Quantum Kernel Methods for Data Processing; and Quantum Gradient Descent for Model Optimization. This component‐centric benchmark suite provides clear guidelines, enabling systematic comparisons, transparent diagnosis of performance bottlenecks, and facilitating the efficient adoption of quantum learning technologies.