mlsanityData to 14 Oct 2026

Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications

arXiv 2505.24765 ↗First posted 30 May 2025quant-ph cs.AIcited by 0

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Abstract (arXiv)

Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel methods, along with hybrid quantum-classical workflows. We examine recent experimental studies that show partial indications of quantum advantage and describe current limitations including noise, barren plateaus, scalability issues, and the lack of formal proofs of performance improvement over classical methods. The main contribution is a ten-year outlook (2025-2035) that outlines possible developments in supervised QML, including a roadmap describing conditions under which QML may be used in applied research and enterprise systems over the next decade.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 30 May 2025.

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References · 25

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Barren plateaus in quantum neural network training landscapescited by 630
  3. 2020Power of data in quantum machine learningcited by 338
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  7. 2017Quantum machine learning: a classical perspectivecited by 95
  8. 2022Exponential concentration in quantum kernel methodscited by 90
  9. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  10. 2023Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracycited by 39
  11. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  12. 2021Quantum machine learning with differential privacycited by 31
13 more
  1. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  2. 2022Federated Learning with Quantum Secure Aggregationcited by 21
  3. 2023A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Modelscited by 16
  4. 2022Hierarchical quantum circuit representations for neural architecture searchcited by 9
  5. 2024Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacitycited by 9
  6. 2024A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learningcited by 8
  7. 2024Physics-Informed Bayesian Optimization of Variational Quantum Circuitscited by 7
  8. 2024Quantum delegated and federated learning via quantum homomorphic encryptioncited by 7
  9. 2024Quantum Natural Language Processingcited by 6
  10. 2025Data re-uploading in Quantum Machine Learning for time series: application to traffic forecastingcited by 5
  11. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  12. 2024Regression and Classification with Single-Qubit Quantum Neural Networkscited by 2
  13. 2024Paving the Way to Hybrid Quantum-Classical Scientific Workflowscited by 1

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