Quantum parallel information exchange (QPIE) hybrid network with transfer learning

Quantum machine learning (QML) has emerged as an innovative framework that has the potential to uncover complex patterns by leveraging the ability of quantum systems to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quantum hardware, as unitary rotations introduce overhead from complex number computations, and quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce a quantum parallel information exchange hybrid network, a new non-sequential hybrid classical quantum model architecture that leverages quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits. This enables efficient pattern recognition and temporal series data prediction by utilizing non-Clifford parameterized quantum gates, thereby enhancing both learning efficiency and representational capacity. Additionally, we developed a dynamic gradient selection method that applies the parameter-shift rule to quantum processing units (QPUs) and adjoint differentiation to graphics processing units (GPUs). Our results demonstrate that the model performance exhibits higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100 -steps, and showing a more unbiased eigenvalue spectrum of the Fisher information matrix on the CPU/GPU and IonQ QPU simulators.