Benchmarking data encoding methods in Quantum Machine Learning

Quantum Machine Learning (QML) is a research area that focuses on the development of Machine Learning (ML) algorithms that can be executed by a quantum computer. Taking advantage of quantum phenomena such as quantum superposition and quantum entanglement, the incorporation of quantum computing in ML aims to leverage the power of the quantum computer to improve existing classical ML algorithms [1]. The process of manipulating the states of qubits by arbitrarily changing the gate parameters for the desired result is closely related to the training process of machine learning algorithms. For solving any specific problem, QML algorithms can be designed as a quantum circuit with a sequence of different quantum gate operations [2][3]. However, Noisy Intermediate Scale Quantum (NISQ) computers are limited in resources and subject to sources of error, such as noise induced by each quantum operation [4][5]. This makes it difficult to develop QML algorithms that perform as well as or better than conventional ones. Hence, developing QML algorithms with good performance, despite today's limited resources, has become a major challenge. To achieve this, it is important to look at several aspects of a QML task, such that the data encoding part. Quantum encoding involves the conversion of classical information into quantum states, enabling QML algorithms to operate efficiently.