Quantum Neural Networks Enhance Flow Model Execution Efficiency
Researchers have developed an efficient quantum circuit for executing flow models using Quantum Neural Networks (QNNs). This new approach aims to improve the precision and reduce the computational resources required for wavefunction flow simulations on quantum computers. The study compares the QNN-based framework with the traditional Quantum Read-Only Memory (QROM)-based framework, demonstrating that the QNN method requires significantly fewer CNOT gates, especially as the number of address qubits increases. For instance, when the number of address qubits increases from 2 to 5, the CNOT gate count for QNNs rises from 192 to 1680, while for QROMs, it escalates exponentially to 39424. This substantial reduction in gate count indicates a more efficient implementation of flow models. The research also shows that the QNN-based phase kickback framework achieves accuracy comparable to the QROM-based framework, with error metrics such as RMSE, RME, KL divergence, and Wasserstein-1 distance showing only slight diff...