Exploration of Optimization Algorithms for Brain-Computer Interfaces Combined with Quantum Computing

Brain-computer interface (BCI) technology, as a key means of human-computer interaction, has a wide range of applications in the fields of neurorehabilitation, brain-control devices, and intelligent medicine. However, existing BCI systems suffer from high computational complexity, low decoding accuracy, and limited real-time performance during neural signal processing, which restricts their practical applications. In this study, a quantum-enhanced BCI optimization algorithm is proposed to address this technical bottleneck by combining the parallel computing capability and nonlinear optimization characteristics of quantum computing. The study constructs a quantum-classical hybrid computing architecture, uses quantum Fourier transform (QFT) to enhance the efficiency of time-frequency analysis of EEG signals, introduces quantum principal component analysis (QPCA) to optimize the dimensionality reduction of high-dimensional data, and enhances the generalization ability of motion intent classification by combining with quantum support vector machines (QSVMs), and verifies the effectiveness of the method in experiments. The results show that the method outperforms the traditional BCI algorithm in terms of decoding accuracy, computational efficiency and real-time performance, and the system end-to-end latency is controlled within 200ms, which meets the demands of online brain-computer interaction. This study provides theoretical support and experimental validation for the application of quantum computing in the field of brain-computer interface, which can be further expanded to closed-loop neuromodulation, intelligent prosthesis control and other application scenarios in the future, providing new ideas for the development of high-efficiency brain-computer interaction technology.