# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Prospects
#### 5.2.2 General Problems  
For quantum computing, many current studies are aimed at a few specific quantum-related problems rather than facing general scientific or engineering problems, both in China and internationally. For example, [PERSON]'s team has made use of quantum computingin molecule ground-state energy solution [25], sequential multiphoton entanglement generation [107], etc.; the Google AI team has utilized quantum computing in isomerization reaction Hartree-Fock simulation [108], topologically ordered state realization [109], and so forth.  
Specifically for QML research, as stated above, most studies are limited to a few certain fields, such as physical science and image processing, and the numerical experiments are usually conducted on special standard datasets. In the future, QML or other quantum computing algorithms may be able to deal with more general problems in various real-world scenarios and play a broader role in promoting the progress of science and technology.  
Here, we would take another important China's national strategic area outside of quantum computing, namely, lunar and planetary exploration, as an example to make further descriptions. In China's Tianwen-1 Mars exploration mission, a payload on Zhurong Mars rover called MarSCoDe has adopted laser-induced breakdown spectroscopy (LIBS) to detect and analyze the chemical composition of the substances on Martian surface [110]. The LIBS spectrum is a kind of high-dimensional data, e.g., each MarSCoDe LIBS spectrum contains over 5000 pixel data points [111]. Additionally, to promote the accuracy of LIBS analysis, it is helpful to integrate other relevant physical parameters into the chemometrics model, such as the plasma temperature and density, the images of plasma, the images of laser ablation crater, and so on [112; 113]. Such data fusion strategies would be beneficial for relieving common problems in LIBS detection, including matrix effects, spectral fluctuation effect, varying-distance effect, dusty surface effect, etc. [114; 115]. Furthermore, the joint use of data from different detection techniques is another trend, e.g., combining LIBS and Raman spectroscopy [116], combining LIBS and remote sensing data [117], etc. These data fusion strategies can improve analytical accuracy, while they would also lead to drastic increase in data dimensionality and volume. For the current data processing scheme (i.e., the on-orbit payloads transmit data back to the Earth laboratory, and the analysts perform subsequent processing in the laboratory), classical computing methodology can meet the requirement of computing speed. However, in application scenes requiring real-time mass data processing (e.g., China's future manned lunar landing mission [118]), classical computing may be hardly competent, and quantum computing may play a shining role at that time. In fact, besides constructing QML models able to efficiently analyze high-dimensional LIBS data and/or different kinds of fused data, quantum computing may also provide key supports for the numerical simulation of laser-plasma evolution dynamics (so that underlying mechanisms of LIBS processes can be better understood and spectral quality can be improved) and for the accurate characterization of LIBS spectral feature differences between Earth lab data and Mars in situ data (so that transfer learning technique can be better utilized and data analysis accuracy can be promoted).