mlsanityData to 14 Oct 2026

Automatic design of quantum feature maps

arXiv 2105.12626 ↗First posted 26 May 2021quant-ph cs.AI cs.LGcited by 28

Not on the map. Why?

Abstract (arXiv)

We propose a new technique for the automatic generation of optimal ad-hoc ans"atze for classification by using quantum support vector machine (QSVM). This efficient method is based on NSGA-II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 26 May 2021.

Builds on

10 of 41 references.

References · 10

  1. 2018Supervised learning with quantum enhanced feature spacescited by 665
  2. 2018Barren plateaus in quantum neural network training landscapescited by 630
  3. 2019Parameterized quantum circuits as machine learning modelscited by 332
  4. 2016Quantum Machine Learningcited by 326
  5. 2021Supervised quantum machine learning models are kernel methodscited by 200
  6. 2018Quantum generative adversarial networkscited by 128
  7. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
  8. 2013Quantum support vector machine for big data classificationcited by 35
  9. 2017Quantum autoencoders via quantum adders with genetic algorithmscited by 19
  10. 2020Natural Evolutionary Strategies for Variational Quantum Computationcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 5; 2024: 6; 2025: 8; 2026: 4.

28 papers.

Sort:

By area:

  1. 2024Quantum Architecture Search: A Surveycited by 32
  2. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  3. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  4. 2023On the expressivity of embedding quantum kernelscited by 17
  5. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  6. 2025A Primer on Quantum Machine Learningcited by 12
  7. 2022Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learningcited by 11
  8. 2022Automatic and effective discovery of quantum kernelscited by 10
  9. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 9
  10. 2022A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmscited by 7
  11. 2024Neural auto-designer for enhanced quantum kernelscited by 7
  12. 2022Compositional optimization of quantum circuits for quantum kernels of support vector machinescited by 6
16 more
  1. 2022AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Imagescited by 4
  2. 2025Quantum Long Short-term Memory with Differentiable Architecture Searchcited by 4
  3. 2025On the similarity of bandwidth-tuned quantum kernels and classical kernelscited by 3
  4. 2023Several fitness functions and entanglement gates in quantum kernel generationcited by 2
  5. 2026Non-variational supervised quantum kernel methods: a reviewcited by 2
  6. 2023A novel feature selection method based on quantum support vector machinecited by 1
  7. 2024Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learningcited by 1
  8. 2024MEMO-QCD: Quantum Density Estimation through Memetic Optimisation for Quantum Circuit Designcited by 1
  9. 2025QuProFS: An Evolutionary Training-free Approach to Efficient Quantum Feature Map Searchcited by 1
  10. 2025Neural Architecture Search Algorithms for Quantum Autoencoderscited by 1
  11. 2025Quantum feature-map learning with reduced resource overheadcited by 1
  12. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  13. 2025AutoQML: A Framework for Automated Quantum Machine Learningcited by 0
  14. 2025Triplet Loss Based Quantum Encoding for Class Separabilitycited by 0
  15. 2026Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modificationscited by 0
  16. 2026Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusioncited by 0