mlsanityData to 7 Oct 2026

A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead

arXiv 2310.10315 ↗First posted 16 Oct 2023quant-ph cs.LGcited by 29

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Abstract (arXiv)

Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 28 of its 166 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

28 of 166 references.

References · 28

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2020Variational Quantum Algorithmscited by 687
  4. 2018Supervised learning with quantum enhanced feature spacescited by 654
  5. 2020Power of data in quantum machine learningcited by 336
  6. 2019Parameterized quantum circuits as machine learning modelscited by 330
  7. 2017Quantum machine learning: a classical perspectivecited by 95
  8. 2020Robust data encodings for quantum classifierscited by 91
  9. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  10. 2020Recurrent Quantum Neural Networkscited by 79
  11. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  12. 2017Machine learning & artificial intelligence in the quantum domaincited by 40
16 more
  1. 2020Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Aheadcited by 34
  2. 2021Entangled Datasets for Quantum Machine Learningcited by 26
  3. 2023Quantum Recurrent Neural Networks for Sequential Learningcited by 24
  4. 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21
  5. 2021Quantum kernels for real-world predictions based on electronic health recordscited by 15
  6. 2022Concentration of Data Encoding in Parameterized Quantum Circuitscited by 15
  7. 2016A Practical Quantum Instruction Set Architecturecited by 9
  8. 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
  9. 2022A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmscited by 7
  10. 2019Optimization of Quantum Circuit Mapping using Gate Transformation and Commutationcited by 6
  11. 2022Compositional optimization of quantum circuits for quantum kernels of support vector machinescited by 6
  12. 2021Quantum Accelerator Stack: A Research Roadmapcited by 4
  13. 2022CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networkscited by 4
  14. 2023Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniquescited by 4
  15. 2019Solving machine learning optimization problems using quantum computerscited by 3
  16. 2021Efficient Noise Mitigation Technique for Quantum Computingcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 10; 2026: 13.

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  1. 2024FedQNN: Federated Learning using Quantum Neural Networkscited by 20
  2. 2024QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detectioncited by 18
  3. 2024QADQN: Quantum Attention Deep Q-Network for Financial Market Predictioncited by 8
  4. 2025A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAGcited by 7
  5. 2025QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challengescited by 7
  6. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  7. 2025PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generationcited by 6
  8. 2025FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithmcited by 5
  9. 2024Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularizationcited by 4
  10. 2025Readout-Side Bypass for Residual Hybrid Quantum-Classical Modelscited by 4
  11. 2024AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modelingcited by 3
  12. 2025Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approachcited by 3
17 more
  1. 2026QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networkscited by 3
  2. 2024ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networkscited by 2
  3. 2025RobQFL: Robust Quantum Federated Learning in Adversarial Environmentcited by 2
  4. 2026Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Diseasecited by 2
  5. 2026Hybrid Quantum-Classical Neural Architecture Searchcited by 1
  6. 2026Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devicescited by 1
  7. 2025Quantum Neural Network applications to Protein Binding Affinity Predictionscited by 0
  8. 2025Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuitscited by 0
  9. 2026Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analyticscited by 0
  10. 2026QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuitscited by 0
  11. 2026PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generationcited by 0
  12. 2026VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?cited by 0
  13. 2026Towards quantum machine learning for assessing the resilience of post-quantum cryptographycited by 0
  14. 2026Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagerycited by 0
  15. 2026Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulationcited by 0
  16. 2026Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approachcited by 0
  17. 2026Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Predictioncited by 0