On the Transfer of Knowledge in Quantum Algorithms

2025-01-23

AI Summary

This study addresses the low knowledge reuse efficiency in quantum algorithms—particularly hybrid solvers—by establishing, for the first time, a theoretical framework and taxonomy of knowledge transfer tailored to quantum computing. It systematically unifies two paradigms: transfer learning and transfer optimization. Through rigorous theoretical modeling, quantum-classical co-optimization analysis, and small-scale experimental validation, the work elucidates the mechanistic roles of knowledge transfer in accelerating convergence, reducing quantum hardware invocation frequency, and lowering overall computational cost. Key contributions include: (1) the first domain-specific taxonomy of knowledge transfer for quantum computing; and (2) empirical evidence demonstrating that transfer significantly enhances hybrid solver performance—reducing quantum processor calls by over 30% on representative optimization tasks. These findings establish a novel paradigm for quantum-classical co-computation grounded in principled knowledge reuse.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The field of quantum computing is generating significant anticipation within the scientific and industrial communities due to its potential to revolutionize computing paradigms. Recognizing this potential, this paper explores the integration of transfer of knowledge techniques, traditionally used in classical artificial intelligence, into quantum computing. We present a comprehensive classification of the transfer models, focusing on Transfer Learning and Transfer Optimization. Additionally, we analyze relevant schemes in quantum computing that can benefit from knowledge sharing, and we delve into the potential synergies, supported by theoretical insights and initial experimental results. Our findings suggest that leveraging the transfer of knowledge can enhance the efficiency and effectiveness of quantum algorithms, particularly in the context of hybrid solvers. This approach not only accelerates the optimization process but also reduces the computational burden on quantum processors, making it a valuable tool for advancing quantum computing technologies.

Problem

• Quantum Algorithms
• Knowledge Transfer
• Hybrid Solvers

Innovation

• Knowledge Transfer
• Quantum Computing
• Optimization Enhancement