Design of an efficient fault-tolerant quantum-computing circuit with quantum neural network learning

Abstract

Fault-tolerance is key to the practical realization of quantum computation, but the design of an efficient, low-overhead and fault-tolerant error-correction circuit remains a major challenge so far. To help address this issue, we first propose a noise-adaptive dissipative quantum neural network (DQNN) model to mitigate the effects of error propagation for constructing a fault-tolerant quantum circuit. Then, we develop a method for preparing a fault-tolerant auxiliary entangled state based on the DQNN model, reducing computational delays and qubit resource consumption. This method utilizes the adaptability of quantum machine learning to the distribution of noisy inputs and its practicality in noisy intermediate scale quantum devices, thus reducing interaction with classical computers and further optimizing the real-time requirements for active error-correction. By integrating quantum error-correction and quantum neural network learning, this DQNN scheme provides a novel solution for constructing scalable fault-tolerant quantum computation. Compared with existing fault-tolerant methods, the DQNN process requires fewer error-propagation efforts and offers higher fidelity in a noisy environment for error thresholds higher than $10^{-4}$. The effectiveness of this method is verified through experimental simulations using the Qiskit. The code for experiments and model in this paper can be found on GitHub: https://github.com/Ricardo-Vv/Qiskit_exam/tree/master.