# On the Transfer of Knowledge in Quantum Algorithms

arXiv: https://arxiv.org/abs/2501.14120 · HTML: https://mlsanity.com/paper/2501.14120/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 100 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 25 of 25)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Training Quantum Embedding Kernels on Near-Term Quantum Computers](https://mlsanity.com/paper/2105.02276/) (2021)
- [Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey](https://mlsanity.com/paper/2002.12169/) (2020)
- [Transfer Learning with Dynamic Adversarial Adaptation Network](https://mlsanity.com/paper/1909.08184/) (2019)
- [Adversarial Training Helps Transfer Learning via Better Representations](https://mlsanity.com/paper/2106.10189/) (2021)
- [Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks](https://mlsanity.com/paper/2007.00017/) (2020)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning](https://mlsanity.com/paper/1710.08904/) (2017)
- [Evolutionary Multitask Optimization: a Methodological Overview, Challenges and Future Research Directions](https://mlsanity.com/paper/2102.02558/) (2021)
- [Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers](https://mlsanity.com/paper/2303.01977/) (2023)
- [Transfer learning of optimal QAOA parameters in combinatorial optimization](https://mlsanity.com/paper/2402.05549/) (2024)
- [Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers](https://mlsanity.com/paper/2403.15114/) (2024)
- [Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning](https://mlsanity.com/paper/2403.05066/) (2024)
- [Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?](https://mlsanity.com/paper/2308.10513/) (2023)
- [Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines](https://mlsanity.com/paper/2311.15966/) (2023)
- [QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems](https://mlsanity.com/paper/2404.15852/) (2024)
- [Quantum Transfer Learning for Acceptability Judgements](https://mlsanity.com/paper/2401.07777/) (2024)

## Built on by (most cited first, 1 of 1)

- [Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share](https://mlsanity.com/paper/2501.15865/) (2025)

## Abstract (arXiv)

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.