Quantum Fourier Transformation Using Quantum Reservoir Computing Network

Abstract

Combining the benefits of quantum computing and artificial neural networks, quantum reservoir computing shows potential for handling complex tasks due to its access to the Hilbert space in exponential dimensions. In this study, the quantum Fourier transform algorithm is implemented utilizing quantum reservoir computing, demonstrating its unique advantages. For the random interactions within the reservoirs, quantum reservoir computing avoids the cost of precise control of the physical system. The proposed model only requires to optimize a linear readout layer, thus significantly reducing the computational cost required for training. The accuracy of the implementation is numerically demonstrated and the model is integrated into quantum circuits to correctly execute the quantum phase estimation algorithm. Additionally, the impacts of different reservoir structures and dissipation intensities within the reservoir, and the results indicate the robustness of the model are discussed.