Quantum parallel information exchange (QPIE) hybrid network with transfer learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915229739253760 |
|---|---|
| author | Guo, Ziqing Khan, Alex Sheng, Victor S. Jabeen, Shabnam Pan, Ziwen |
| author_facet | Guo, Ziqing Khan, Alex Sheng, Victor S. Jabeen, Shabnam Pan, Ziwen |
| contents | Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns by leveraging quantum systems ability to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network (QNN) 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 the quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce quantum parallel information exchange (QPIE) hybrid network, a new non-sequential hybrid classical quantum model architecture, leveraging quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits, which 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 develop a dynamic gradient selection method that applies the parameter shift rule on quantum processing units (QPUs) and adjoint differentiation on GPUs. Our results demonstrate model performance exhibiting higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100-steps, and showcasing a more unbaised eigenvalue spectrum of the fisher information matrix on CPU/GPU and IonQ QPU simulators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum parallel information exchange (QPIE) hybrid network with transfer learning Guo, Ziqing Khan, Alex Sheng, Victor S. Jabeen, Shabnam Pan, Ziwen Quantum Physics Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns by leveraging quantum systems ability to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network (QNN) 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 the quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce quantum parallel information exchange (QPIE) hybrid network, a new non-sequential hybrid classical quantum model architecture, leveraging quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits, which 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 develop a dynamic gradient selection method that applies the parameter shift rule on quantum processing units (QPUs) and adjoint differentiation on GPUs. Our results demonstrate model performance exhibiting higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100-steps, and showcasing a more unbaised eigenvalue spectrum of the fisher information matrix on CPU/GPU and IonQ QPU simulators. |
| title | Quantum parallel information exchange (QPIE) hybrid network with transfer learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.04235 |