Quantum circuit output prediction based on time-series neural network integration
Xiang Li, Xueyun Cheng, Xinyu Chen, Zhijin Guan, Pengcheng Zhu, Hui Gu · 2024
In the context of current Noisy Intermediate-Scale Quantum (NISQ) computers, the application of quantum circuits is hampered by various sources of noise, resulting in errors between the actual output and the ideal output. To mitigate these errors, this study investigates a method for predicting the expected output of quantum circuits. In order to enhance the precision of the experiments, a fixed-scale quantum circuit dataset is randomly collected, and one-hot encoding is applied to represent the data. To eliminate redundant noise in the dataset, a Quantum auto-encoder (QAE) reconstruction encoding model is designed to separate and reconstruct the descriptions of quantum circuits in the dataset. The operation process of a quantum circuit is a sequence of quantum gate operations, and gate operations are gradually realized to change the state of qubits. We regard the process of quantum circuit implementation as a process of evolution with time, so it has the nature of timing. We build an additive fusion prediction model based on temporal neural network, the model can be trained to learn the relationship between sequences to predict the output of quantum circuit, and a loss function based on maximum likelihood estimation is employed to optimize the model parameters. Additionally, a dual approach is verified to optimize the computational indicators in the ensemble model. To validate the effectiveness of the proposed method, experiments are conducted on quantum circuits of varying scales, and the results are compared with the quantum system framework provided by IBM Quantum Experience. The experimental results show that the time series neural network integration can better highlight timing information and noise information in quantum circuits, the proposed method outperforms the IBM Quantum Experience framework in terms of circuit depth and network depth for quantum circuits of different scales, exhibiting robustness and stability.