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Transfer learning in hybrid classical-quantum neural networks

arXiv 1912.08278 ↗First posted 17 Dec 2019quant-ph cs.LG stat.MLcited by 114

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Abstract (arXiv)

We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti.

Key facts. Cited by 114 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2019.

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Citing papers per year: 2021: 16; 2022: 16; 2023: 21; 2024: 15; 2025: 22; 2026: 24.

114 papers.

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  1. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 85
  2. 2021Federated Quantum Machine Learningcited by 82
  3. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 60
  4. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  5. 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
  6. 2022Systematic Literature Review: Quantum Machine Learning and its applicationscited by 32
  7. 2021Quantum machine learning with differential privacycited by 31
  8. 2021Exponentially Many Local Minima in Quantum Neural Networkscited by 22
  9. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  10. 2023Let Quantum Neural Networks Choose Their Own Frequenciescited by 22
  11. 2021Hybrid Quantum-Classical Graph Convolutional Networkcited by 21
  12. 2021Quantum Self-Supervised Learningcited by 20
38 more
  1. 2023Towards Quantum Federated Learningcited by 20
  2. 2022Classical-to-quantum convolutional neural network transfer learningcited by 18
  3. 2023Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapescited by 18
  4. 2022Hybrid quantum ResNet for car classification and its hyperparameter optimizationcited by 17
  5. 2023Federated Quantum Machine Learning with Differential Privacycited by 17
  6. 2021Variational Quantum Soft Actor-Criticcited by 15
  7. 2021Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)cited by 14
  8. 2022Reservoir Computing via Quantum Recurrent Neural Networkscited by 14
  9. 2021Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Seriescited by 10
  10. 2023Forecasting steam mass flow in power plants using the parallel hybrid networkcited by 10
  11. 2025Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networkscited by 10
  12. 2024Quantum Advantage Actor-Critic for Reinforcement Learningcited by 9
  13. 2024Quantum Gradient Class Activation Map for Model Interpretabilitycited by 9
  14. 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
  15. 2021Quantum Embedding Search for Quantum Machine Learningcited by 7
  16. 2022Completely Quantum Neural Networkscited by 7
  17. 2025Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecastingcited by 7
  18. 2022A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographscited by 6
  19. 2023Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processingcited by 6
  20. 2023What can we learn from quantum convolutional neural networks?cited by 6
  21. 20233D-QAE: Fully Quantum Auto-Encoding of 3D Point Cloudscited by 5
  22. 2025Data re-uploading in Quantum Machine Learning for time series: application to traffic forecastingcited by 5
  23. 2021Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detectioncited by 4
  24. 2022Random Quantum Neural Networks (RQNN) for Noisy Image Recognitioncited by 4
  25. 2022Quantum Transfer Learning for Wi-Fi Sensingcited by 4
  26. 2022AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Imagescited by 4
  27. 2023SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Trainingcited by 4
  28. 2023Variational Quantum Neural Networks (VQNNS) in Image Classificationcited by 4
  29. 2023Hybrid quantum transfer learning for crack image classification on NISQ hardwarecited by 4
  30. 2022Efficient Quantum Feature Extraction for CNN-based Learningcited by 3
  31. 2022Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimizationcited by 3
  32. 2023Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architecturescited by 3
  33. 2023Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillationcited by 3
  34. 2023Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machinescited by 3
  35. 2023A Novel Image Classification Framework Based on Variational Quantum Algorithmscited by 3
  36. 2025Transfer Learning Analysis of Variational Quantum Circuitscited by 3
  37. 2025Quantum-Enhanced Attention Mechanism in NLP: A Hybrid Classical-Quantum Approachcited by 3
  38. 2026MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learningcited by 3

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