Name
Abstract | ||
|---|---|---|
In machine learning, classification algorithms often use statistical methods to build the correspondence between features (or attributes) and categories (or labels), that is, the statistical correlation between features and categories. In quantum theory, a large number of experimental results show that quantum correlation is far stronger than what can be explained by local hidden theory (i.e., classical or non-quantum theory), that is, quantum mechanics theory reveals a statistical correlation stronger than that described by classical theory. Based on this, this paper will use the strong statistical correlation revealed by Bell state to build a classification algorithm to verify the validity and superiority of the formal framework of quantum mechanics in specific classification tasks. Specifically, we use quantum joint probabilities derived from the measurement process of Bell state to model the quantum statistical correlation between features and categories. The paper first theoretically proves that the formal framework used has the ability to violate Bell inequality; moreover, a classification algorithm is implemented and verified on classic machine learning datasets. Experimental results show that the algorithm is significantly better than most mainstream machine learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IJCNN52387.2021.9534416 | 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) |
Keywords | DocType | ISSN |
Quantum Machine Learning, Quantum Algorithm, Bell State, Quantum Correlation | Conference | 2161-4393 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junwei Zhang | 1 | 0 | 1.01 |
| Rui-Fang He | 2 | 23 | 5.43 |
| Zhao Li | 3 | 118 | 29.10 |
| Ji Zhang | 4 | 218 | 19.43 |
| Biao Wang | 5 | 1 | 1.70 |
| Zhaolin Li | 6 | 0 | 0.68 |
| Tianvuan Niu | 7 | 0 | 0.34 |