# An Overview of Quantum Machine Learning Research in China
## 1 Introduction  
As a major area in artificial intelligence, machine learning has become a hotspot technique in both academic and industrial fields, extensively applied in quite a few areas including image recognition, data mining, medical care, natural language processing, and so forth [1]. In machine learning algorithms, data are often stored in matrices [2]. However, with the exponential growth of the amount of data and the increase in algorithm model volume (e.g., in the popular GPT-4 model, there are 1.76 trillion parameters to be trained [3, 4]), the processing efficiency of classical machine learning rapidly declines since conventional computers require a great amount of time and computing resources to perform matrix operations [1, 2]. In the future era of data explosion, classical machine learning algorithms will face more severe challenges in processing massive data, and the utilization of quantum computing is a highly anticipated solution. Quantum computing is a methodology of computation that makes use of quantum phenomena in physics [5]. The initial idea originated in 1982 when physicist [PERSON] pointed out that quantum computers might have advantages that classical computers could not parallel in solving specific problems [6]. These advantages are achieved based on quantum properties, such as quantum superposition and quantum entanglement [5]. They make quantum computers superior to classical computers in terms of computation cost,and theoretically quantum computing can solve extremely complex problems that classical computing is impossibly able to deal with [7]. In the 1990s, [PERSON] (from Bell Laboratory) proposed an integer factorization algorithm based on quantum computing, which has an exponential advantage compared to classical factorization algorithms [8]; [PERSON] (also from Bell Laboratory) proposed a quantum search algorithm that can realize square-level acceleration compared with classical search algorithm [9]. In 2008, [PERSON] et al. proposed the well-known HHL algorithm for solving linear systems of equations, which achieved exponential acceleration against classical solutions [10]. These early achievements have laid the foundation for the development of quantum computing. Quantum machine learning (QML) is a technique that combines the advantages of quantum computing and machine learning, aiming to solve specific difficult problems in classical machine learning based on quantum computing methods [11; 12; 13; 14]. The fundamental idea of QML is to utilize quantum advantages, i.e., the superposition and/or entanglement of quantum bits (qubits), to accelerate the training process of machine learning [5]. A classical bit can only be in one of two binary states (0 or 1), while a qubit can stand in a superposition state of \(\ket{0}\) and \(\ket{1}\). Therefore, the quantum superposition property can provide QML algorithms with extraordinary parallel processing capabilities and, hence, realize exponential acceleration compared to classical algorithms [15]. As to the quantum entanglement property, it can help us generate qubits in entangled states, such as Bell states. Bell states are a set of maximally entangled states in quantum mechanics that describe two-qubit systems. The maximum entanglement orthogonal basis of a two-qubit system is composed of four Bell states, with their mathematical expressions being \(\ket{\Phi^{\pm}}=(\ket{00}\ \pm\ \ket{11})/\sqrt{2}\) and \(\ket{\Psi^{\pm}}=(\ket{01}\ \pm\ \ket{10})/\sqrt{2}\). For two qubits in Bell states, measuring the state of one qubit can instantly determine the state of the other, hence realizing the non-local correlation. This property can be beneficial to simplifying the construction two-qubit operation gate, such as controlled NOT (CNOT) gate [16].