On the Transfer of Knowledge in Quantum Algorithms
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_ | 1866911300889608192 |
|---|---|
| author | Villar-Rodriguez, Esther Osaba, Eneko Oregi, Izaskun Romero, Sebastián V. Ferreiro-Vélez, Julián |
| author_facet | Villar-Rodriguez, Esther Osaba, Eneko Oregi, Izaskun Romero, Sebastián V. Ferreiro-Vélez, Julián |
| contents | Quantum computing is poised to transform computational paradigms across science and industry. As the field evolves, it can benefit from established classical methodologies, including promising paradigms such as Transfer of Knowledge (ToK). This work serves as a brief, self-contained reference for ToK, unifying its core principles under a single formal framework. We introduce a joint notation that consolidates and extends prior work in Transfer Learning and Transfer Optimization, bridging traditionally separate research lines and enabling a common language for knowledge reuse. Building on this foundation, we classify existing ToK strategies and principles into a structured taxonomy that helps researchers position their methods within a broader conceptual map. We then extend key transfer protocols to quantum computing, introducing two novel use cases--reverse annealing and multitasking Quantum Approximate Optimization Algorithm (QAOA)--alongside a sequential Variational Quantum Eigensolver (VQE) approach that supports and validates prior findings. These examples highlight ToK's potential to improve performance and generalization in quantum algorithms. Finally, we outline challenges and opportunities for integrating ToK into quantum computing, emphasizing its role in reducing resource demands and accelerating problem-solving. This work lays the groundwork for future synergies between classical and quantum computing through a shared, transferable knowledge framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14120 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Transfer of Knowledge in Quantum Algorithms Villar-Rodriguez, Esther Osaba, Eneko Oregi, Izaskun Romero, Sebastián V. Ferreiro-Vélez, Julián Quantum Physics Artificial Intelligence Quantum computing is poised to transform computational paradigms across science and industry. As the field evolves, it can benefit from established classical methodologies, including promising paradigms such as Transfer of Knowledge (ToK). This work serves as a brief, self-contained reference for ToK, unifying its core principles under a single formal framework. We introduce a joint notation that consolidates and extends prior work in Transfer Learning and Transfer Optimization, bridging traditionally separate research lines and enabling a common language for knowledge reuse. Building on this foundation, we classify existing ToK strategies and principles into a structured taxonomy that helps researchers position their methods within a broader conceptual map. We then extend key transfer protocols to quantum computing, introducing two novel use cases--reverse annealing and multitasking Quantum Approximate Optimization Algorithm (QAOA)--alongside a sequential Variational Quantum Eigensolver (VQE) approach that supports and validates prior findings. These examples highlight ToK's potential to improve performance and generalization in quantum algorithms. Finally, we outline challenges and opportunities for integrating ToK into quantum computing, emphasizing its role in reducing resource demands and accelerating problem-solving. This work lays the groundwork for future synergies between classical and quantum computing through a shared, transferable knowledge framework. |
| title | On the Transfer of Knowledge in Quantum Algorithms |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.14120 |