mlsanityData to 7 Oct 2026

Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications

arXiv 2307.00908 ↗First posted 3 Jul 2023quant-ph cs.LG stat.MLcited by 14

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Abstract (arXiv)

The past decade has witnessed significant advancements in quantum hardware, encompassing improvements in speed, qubit quantity, and quantum volume-a metric defining the maximum size of a quantum circuit effectively implementable on near-term quantum devices. This progress has led to a surge in Quantum Machine Learning (QML) applications on real hardware, aiming to achieve quantum advantage over classical approaches. This survey focuses on selected supervised and unsupervised learning applications executed on quantum hardware, specifically tailored for real-world scenarios. The exploration includes a thorough analysis of current QML implementation limitations on quantum hardware, covering techniques like encoding, ansatz structure, error mitigation, and gradient methods to address these challenges. Furthermore, the survey evaluates the performance of QML implementations in comparison to classical counterparts. In conclusion, we discuss existing bottlenecks related to applying QML on real quantum devices and propose potential solutions to overcome these challenges in the future.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 100 of its 426 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

100 of 426 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 9; 2026: 1.

14 papers.

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  1. 2023Quantum-Inspired Machine Learning: a Surveycited by 11
  2. 2025A Primer on Quantum Machine Learningcited by 9
  3. 2023sQUlearn -- A Python Library for Quantum Machine Learningcited by 3
  4. 2024Quantum Supervised Learningcited by 3
  5. 2025Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensemblescited by 3
  6. 2024Regression and Classification with Single-Qubit Quantum Neural Networkscited by 2
  7. 2025Density Operator Expectation Maximizationcited by 1
  8. 2025Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learningcited by 1
  9. 2025LLM-Guided Ans"atze Design for Quantum Circuit Born Machines in Financial Generative Modelingcited by 1
  10. 2025PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimizationcited by 1
  11. 2025Quantum Computing Methods for Malware Detectioncited by 1
  12. 2025Single-Qudit Quantum Neural Networks for Multiclass Classificationcited by 0
2 more
  1. 2025On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generatorscited by 0
  2. 2026Quantum Scrambling Born Machinecited by 0