On the Transfer of Knowledge in Quantum Algorithms
This work provides a foundational framework for researchers in quantum computing to leverage classical knowledge transfer methods, though it is incremental as it builds on existing paradigms.
The paper tackles the problem of applying Transfer of Knowledge (ToK) from classical computing to quantum algorithms by unifying principles into a formal framework and extending transfer protocols, with examples like reverse annealing and multitasking QAOA that highlight potential performance improvements.
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 QAOA) alongside a sequential 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.