Re-structuring CNN using quantum layer executed on FPGA hardware for classifying 2-D data
Nhat Hoang Bach, Le Ha Vu, Dinh Lam Tran, Thanh Toan Dao, Thi Thu Hong Luu, Duy Ninh Nguyen · 2024
The article proposes a solution to restructure Convolutional Neural Network (CNN) architectures by integrating parameter quantization techniques with traditional CNN models capable of deployment on Field-Programmable Gate Array (FPGA) hardware for evaluating the classification performance of two-dimensional data in real-world scenarios. The solution introduces an additional quantum layer before the final classification layer of the CNN to receive standardized outputs, followed by computations in the Hilbert vector space to generate probability values for assessing classification results. The quantization process helps the model swiftly identify data features while optimizing the parallel computing capability of FPGA hardware. The model is evaluated on the MNIST handwritten digit dataset, revealing two advantages: time-processing on FPGA is four times faster compared to using only the Central Processing Unit (CPU) on the PynQ-z2 kit board; classification accuracy is higher when utilizing the quantum layer compared to without it, with the same number of training iterations. These results demonstrate the feasibility of hardware-accelerated AI algorithms combined with quantum algorithms in real applications.