Evolution from classical to quantum-based models in Artificial Intelligence

Abstract

Several research works have demonstrated the effectiveness of AI algorithms, but the state-of-the-art algorithms are based on the classical theories of probability and logic. Quantum Mechanics (QM) has already shown its effectiveness in many fields, and researchers have proposed several interesting results which cannot be obtained through classical theory. In recent years, researchers have been trying to investigate whether the QM can help to improve the machine learning algorithms. It is believed that the theory of QM may also inspire an effective algorithm if it is adequately implemented. From this inspiration, we proposed a novel quantum-based classification method and quantum kernel method, and exploited quantum-like models in relevance judgment and decision-making tasks. For the obtained experimental results, our proposed quantum-based approach can outperform existing classical counterparts approaches.

Further info available at: 

https://dl.acm.org/doi/abs/10.1145/3269206.3269304

https://ieeexplore.ieee.org/abstract/document/8671690

https://arxiv.org/abs/2007.07887

https://link.springer.com/chapter/10.1007/978-3-030-45439-5_48