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Quantum Adversarial Transfer Learning.

Longhan Wang1, Yifan Sun1, Xiangdong Zhang1

  • 1Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology, Beijing 100081, China.

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Quantum adversarial transfer learning uses quantum states for machine learning across different datasets. This approach offers exponential advantages in computing resources and storage over classical methods.

Keywords:
quantum computationquantum generative adversarial networkquantum machine learningquantum transfer learning

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Area of Science:

  • Quantum computing
  • Machine learning
  • Artificial intelligence

Background:

  • Adversarial transfer learning (ATL) addresses challenges with insufficient target data by learning across domains.
  • ATL utilizes adversarial training for domain adaptation.
  • Classical ATL methods face limitations in computational efficiency and data storage.

Purpose of the Study:

  • Introduce quantum adversarial transfer learning (QATL).
  • Explore the application of quantum mechanics to adversarial transfer learning.
  • Demonstrate the potential of QATL for enhanced efficiency and performance.

Main Methods:

  • Data encoded entirely as quantum states.
  • Measurement-based data labeling.
  • Quantum subroutine for gradient computation.
  • Analysis of computational resource requirements (gate number, storage size).

Main Results:

  • QATL demonstrates an exponential advantage over classical methods in computing resources.
  • Numerical experiments confirm successful training of the QATL model.
  • High accuracy achieved on specific datasets.

Conclusions:

  • QATL offers a promising new paradigm for machine learning.
  • Quantum computation can significantly enhance adversarial transfer learning.
  • QATL provides a scalable and efficient solution for domain adaptation problems.