将知识迁移引入量子算法，提升性能与通用性。
On the Transfer of Knowledge in Quantum Algorithms
- 统一经典迁移学习与优化框架，构建量子适用的通用符号体系。
- 提出反向绝热与多任务QAOA等新应用，验证迁移有效性。
- 适合量子算法研究者及希望降低资源消耗的从业者。
量子计算有望重塑科学与工业的计算范式。随着领域发展，可借鉴经典方法中的知识迁移（ToK）理念。本文作为自包含参考，将迁移学习与迁移优化的核心原则整合进统一形式框架，引入联合符号体系，弥合传统上分离的研究方向，实现知识复用的共通语言。基于此，我们构建了现有迁移策略的结构化分类体系，帮助研究者定位自身方法。进一步将关键迁移协议拓展至量子计算，提出两种新应用场景——反向绝热与多任务量子近似优化算法（QAOA），并引入一种支持验证的顺序变分量子本征值求解器（VQE）方法。这些案例凸显了知识迁移在提升量子算法性能与泛化能力方面的潜力。最后，我们概述了融入知识迁移所面临的挑战与机遇，强调其在降低资源消耗与加速求解方面的作用。本文为经典与量子计算间通过可迁移知识框架实现未来协同奠定基础。
原文摘要 · Abstract (English)
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.
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