Training Quantum Self-Attention Model in Near-Term Quantum Computer

Quantum computing is rapidly emerging as a revolutionary computing paradigm with significant development potential. Variational quantum algorithms, especially those employing variational quantum circuits, have demonstrated notable advantages in domains including finance, chemistry, and machine learning. However, the practical deployment of these algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices faces challenges due to quantum noise, state collapse, and limited qubits numbers. The real-world performance of these algorithms remains largely unexplored. This paper addresses this gap by proposing and implementing a novel method for training quantum self-attention model for text classification directly on quantum computing chips. This initiative marks the first instance of employing such models on actual quantum hardware. Our approach optimizes quantum circuit designs to mitigate the impact of quantum noise and introduces parallel training strategies to enhance efficiency. Our experiments, conducted on the “Wukong” superconducting 72-qubit quantum computer, demonstrate that our models can be effectively trained on real quantum hardware. The results from the actual quantum chips slightly outperformed those from simulators, confirming the practical feasibility of applying quantum self-attention model to natural language processing tasks. These results validate the effectiveness of our approach and provide valuable insights for the future deployment of quantum machine learning models.