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On the Transfer of Knowledge in Quantum Algorithms

Created by
  • Haebom

Author

Esther Villar-Rodriguez, Eneko Osaba, Izaskun Oregi, Sebasti an V. Romero, Juli an Ferreiro- Velez

Outline

This paper addresses the application of classical transfer of knowledge (ToK) methodology to the field of quantum computing. In particular, we present a common formal framework that integrates transfer learning and transfer optimization, and systematically categorize existing ToK strategies to help researchers understand their methodology in a broader context. In addition, we present novel quantum computing applications such as inverse annealing, multitask QAOA, and sequential VQE approaches, demonstrating the potential of ToK. Finally, we present the challenges and opportunities of integrating ToK into quantum computing, emphasizing its potential to reduce resource requirements and improve problem-solving speed.

Takeaways, Limitations

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Takeaways:
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A concrete example showing the utility of transfer of knowledge (ToK) in quantum computing.
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Provides a common formal framework to integrate transfer learning and transfer optimization.
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Provides a taxonomy that systematically classifies existing ToK strategies.
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It suggests that it can contribute to improving the performance and generalization of quantum algorithms.
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Suggests the potential to contribute to reducing quantum computing resource requirements and increasing problem-solving speed.
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Limitations:
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The proposed quantum computing applications are still limited.
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More extensive experimental validation of ToK's quantum computing applications is needed.
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Further research is needed on the applicability and efficiency to various quantum algorithms and hardware platforms.
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