A survey on quantum data mining algorithms: challenges, advances and future directions

Data mining has reached a state that is difficult to break through, while the scale of data is growing rapidly, due to the lack of traditional computing power and limited data storage space. Efficient and accurate extraction of valuable information from massive data has become a challenge. Researchers have combined quantum computing with data mining to address this problem, hence the concept of quantum data mining has emerged. The fundamental tenets of quantum physics are adhered to for information transmission and computing operations in quantum data mining, which use the states of minuscule particles to represent and process information. Quantum data mining are based on the characteristics of quantum computing, such as superposition and entanglement, which make the ability of computational and information extraction effectively improved. The paper discusses and summarizes the relevant literature on quantum data mining in recent 3 years. After introducing relevant basic concepts of quantum computing, quantum data mining is presented in five aspects: quantum data classification, quantum data clustering, quantum dimensionality reduction, quantum association rules, quantum linear regression, and quantum causal analysis. These approaches, based on quantum computing, offer new perspectives and tools for handling complex data mining tasks. In conclusion, the development of quantum data mining is promising and crucial to overcome the difficulties associated with large-scale data mining.