mlsanityData to 7 Oct 2026

Robust data encodings for quantum classifiers

arXiv 2003.01695 ↗First posted 3 Mar 2020quant-ph cs.LGcited by 91

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Abstract (arXiv)

Data representation is crucial for the success of machine learning models. In the context of quantum machine learning with near-term quantum computers, equally important considerations of how to efficiently input (encode) data and effectively deal with noise arise. In this work, we study data encodings for binary quantum classification and investigate their properties both with and without noise. For the common classifier we consider, we show that encodings determine the classes of learnable decision boundaries as well as the set of points which retain the same classification in the presence of noise. After defining the notion of a robust data encoding, we prove several results on robustness for different channels, discuss the existence of robust encodings, and prove an upper bound on the number of robust points in terms of fidelities between noisy and noiseless states. Numerical results for several example implementations are provided to reinforce our findings.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2020.

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Citing papers per year: 2021: 10; 2022: 19; 2023: 17; 2024: 14; 2025: 23; 2026: 8.

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  1. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  2. 2021Recent advances for quantum classifierscited by 35
  3. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  4. 2022Systematic Literature Review: Quantum Machine Learning and its applicationscited by 32
  5. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  6. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
  7. 2022Benchmarking Adversarially Robust Quantum Machine Learning at Scalecited by 28
  8. 2021Entangled Datasets for Quantum Machine Learningcited by 26
  9. 2023Towards quantum enhanced adversarial robustness in machine learningcited by 26
  10. 2023QKSAN: A Quantum Kernel Self-Attention Networkcited by 23
  11. 2023Let Quantum Neural Networks Choose Their Own Frequenciescited by 22
  12. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
38 more
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  5. 2021Variational inference with a quantum computercited by 18
  6. 2022Classical-to-quantum convolutional neural network transfer learningcited by 18
  7. 2022Enhancing Quantum Adversarial Robustness by Randomized Encodingscited by 16
  8. 2022Policy Gradients using Variational Quantum Circuitscited by 15
  9. 2022Concentration of Data Encoding in Parameterized Quantum Circuitscited by 15
  10. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14
  11. 2022Reservoir Computing via Quantum Recurrent Neural Networkscited by 14
  12. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  13. 2024QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Designcited by 14
  14. 2022Incremental Data-Uploading for Full-Quantum Classificationcited by 13
  15. 2022Avoiding Barren Plateaus with Classical Deep Neural Networkscited by 13
  16. 2022Quantum Neural Network Compressioncited by 12
  17. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  18. 2025A Primer on Quantum Machine Learningcited by 9
  19. 2022Quantum neural network autoencoder and classifier applied to an industrial case studycited by 8
  20. 2022Differential Privacy Amplification in Quantum and Quantum-inspired Algorithmscited by 6
  21. 2023The quantum cost function concentration dependency on the parametrization expressivitycited by 6
  22. 2024Adversarial Robustness Guarantees for Quantum Classifierscited by 6
  23. 2022Evolution strategies: Application in hybrid quantum-classical neural networkscited by 5
  24. 2023Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applicationscited by 5
  25. 2024Generating Universal Adversarial Perturbations for Quantum Classifierscited by 5
  26. 2024Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attentioncited by 5
  27. 2024Anomaly Detection for Real-World Cyber-Physical Security using Quantum Hybrid Support Vector Machinescited by 5
  28. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  29. 2025Efficient Quantum Convolutional Neural Networks for Image Classification: Overcoming Hardware Constraintscited by 5
  30. 2021Accelerating variational quantum algorithms with multiple quantum processorscited by 4
  31. 2023SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Trainingcited by 4
  32. 2023QTrojan: A Circuit Backdoor Against Quantum Neural Networkscited by 4
  33. 2021F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuitscited by 3
  34. 2025Predictive Performance of Deep Quantum Data Re-uploading Modelscited by 3
  35. 2021Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelitycited by 2
  36. 2022Beyond Ans"atze: Learning Quantum Circuits as Unitary Operatorscited by 2
  37. 2023Data is often loadable in short depth: Quantum circuits from tensor networks for finance, images, fluids, and proteinscited by 2
  38. 2023Radio Signal Classification by Adversarially Robust Quantum Machine Learningcited by 2

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