The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
This paper explores the application of quantum neural networks (QNNs) to enhance pattern recognition capabilities, particularly in the analysis of complex datasets. Traditional neural networks often struggle with non-linear data patterns due to limitations in their ability to capture intricate relationships. Quantum neural networks, leveraging quantum mechanical principles, offer a potential paradigm shift in pattern recognition by introducing inherent quantum properties that facilitate novel feature extraction and pattern understanding. We propose a novel QNN architecture designed to automatically learn hidden patterns and relationships within data, aiming to surpass the capabilities of conventional neural networks. This work investigates the potential of quantum-enhanced neural networks for identifying complex, non-linear patterns, demonstrating its efficacy through simulation and preliminary experimentation. The core mechanism involves utilizing quantum entanglement and superposition to represent and process data in a fundamentally different manner, ultimately leading to improved pattern recognition accuracy.