# An Overview of Quantum Machine Learning Research in China
## 1 Introduction
Therefore, the quantum entanglement property can realize more complicated computational operations for various QML applications, such as constructing loss functions through entanglement effects [17], establishing quantum teleportation protocols for secure QML [18], and implementing quantum competition based on entanglement measures [19; 20]. The basic principles of computational acceleration brought by quantum superposition and quantum entanglement are illustrated in Figure 1.  
Figure 1: Schematic diagram of the principle of computational acceleration based on quantum superposition and quantum entanglement. Quantum superposition enables \(N\) qubits to represent \(2^{N}\) superposition states, hence allowing quantum computing parallelism (i.e., one quantum operation is equivalent to \(2^{N}\) classical operations). Quantum entanglement enables the change in one of two entangled qubits (such as qubits in Bell states) to synchronously affect the other qubit, hence allowing for more complicated interactions and operations. Quantum computing may help classical machine learning at various stages, such as data collection/preprocessing, feature engineering, model training, and model validation/optimization. For data collection/preprocessing, assuming each sample is an \(N\)-dimensional data, in the classical framework, one cannot directly alter the data dimensionality. In quantum computing, however, one may employ quantum state encoding techniques like amplitude encoding to map such data, requiring only \(\log_{2}N\) qubits to represent the \(N\)-dimensional data. This exponential data compression can drastically reduce computational cost. As to feature engineering, classical approaches like principal component analysis (PCA) rely on covariance matrix calculation and eigenvalue decomposition, with the time complexity scaling as \(O\big{(}N^{3}\big{)}\), meaning that the computational time increases drastically with data dimensionality. In contrast, quantum PCA (QPCA) can theoretically leverage quantum state preparation and quantum phase estimation to achieve a time complexity of \(O(\text{poly}(\log N))\)[21], significantly accelerating the feature dimensionality reduction. Regarding model training, taking neural network methodology as an example, classical training methods such as stochastic gradient descent compute gradients for each weight parameter sequentially. For deep learning networks with billions of weight parameters, the training can be dramatically time-consuming. Quantum computing can address this issue by carrying out parallel computing to compute gradients for all parameters simultaneously (e.g., parameterized quantum circuits [22]) or by improving parameter updating strategy to compute the best updating directions in the geometric structure of quantum state space (e.g., quantum natural gradient [23]). For model validation, classical approaches need to validate multiple dataset partition schemes one by one, whereas quantum methods can leverage quantum superposition to validate multiple schemes in parallel. With regard to model optimization, classical methods need to attempt a great many hyperparameter combinations to search the optimal combination set. By employing quantum searching algorithms, such as quantum annealing [24] and Grover's algorithm [9], the required number of searching iterations can be considerably reduced, and the computation time can be significantly saved. In recent years, the quantum science and technology in China has flourished, with a constant stream of high-level scientific and technological achievements. For example, [PERSON]'s team has made important progress in quantum computing fields such as quantum computational chemistry [25] and quantum walk [26], as well as in ultra-distant satellite communication based on quantum key distribution by collaborating with [PERSON]'s team [27; 28]. These achievements have made significant contributions not only to the quantum field in China but also the whole quantum realm throughout the world.