mlsanityData to 14 Oct 2026

Variational Quantum Approximate Support Vector Machine with Inference Transfer

arXiv 2206.14507 ↗First posted 29 Jun 2022quant-ph cs.LGcited by 7

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Abstract (arXiv)

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 12 of its 58 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

12 of 58 references.

References · 12

  1. 2021Convex optimizationcited by 2,515
  2. 2020Variational Quantum Algorithmscited by 692
  3. 2018Supervised learning with quantum enhanced feature spacescited by 665
  4. 2018Barren plateaus in quantum neural network training landscapescited by 630
  5. 2019Parameterized quantum circuits as machine learning modelscited by 332
  6. 2016Quantum Machine Learningcited by 326
  7. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  8. 2021Generalization in quantum machine learning from few training datacited by 216
  9. 2021Supervised quantum machine learning models are kernel methodscited by 200
  10. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  11. 2020Reformulation of the No-Free-Lunch Theorem for Entangled Data Setscited by 28
  12. 2020A divide-and-conquer algorithm for quantum state preparationcited by 26

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Cited by year (2026 to 14 Oct)

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

7 papers.

  1. 2023Enhancing Quantum Support Vector Machines through Variational Kernel Trainingcited by 14
  2. 2023Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problemscited by 2
  3. 2025QuXAI: Explainers for Hybrid Quantum Machine Learning Modelscited by 2
  4. 2025Multi-channel convolutional neural quantum embeddingcited by 1
  5. 2024Computable Model-Independent Bounds for Adversarial Quantum Machine Learningcited by 0
  6. 2026Spectral Phase Encoding for Quantum Kernel Methodscited by 0
  7. 2026Quantum inspired qubit qutrit neural networks for real time financial forecastingcited by 0