mlsanityData to 14 Oct 2026

QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers

arXiv 2309.15056 ↗First posted 26 Sep 2023quant-ph cs.LGcited by 11

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Abstract (arXiv)

Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits that are also error prone. Quilt is a framework for performing multi-class classification task designed to work effectively on current error-prone quantum computers. Quilt is evaluated with real quantum machines as well as with projected noise levels as quantum machines become more noise-free. Quilt demonstrates up to 85% multi-class classification accuracy with the MNIST dataset on a five-qubit system.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 13 of its 38 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

13 of 38 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2018Supervised learning with quantum enhanced feature spacescited by 665
  4. 2019Parameterized quantum circuits as machine learning modelscited by 332
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  6. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 98
  7. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  8. 2019Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspectivecited by 73
  9. 2019Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problemscited by 31
  10. 2020VSQL: Variational Shadow Quantum Learning for Classificationcited by 17
  11. 2019Image classification using quantum inference on the D-Wave 2Xcited by 15
  12. 2019Quantum Speedup in Adaptive Boosting of Binary Classificationcited by 3
1 more
  1. 2019Binary Classifier Inspired by Quantum Theorycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 2; 2026: 2.

11 papers.

  1. 2024'Eliv'agar: Efficient Quantum Circuit Search for Classificationcited by 12
  2. 2023Toward Automated Quantum Variational Machine Learningcited by 3
  3. 2024Multi-Class Quantum Convolutional Neural Networkscited by 3
  4. 2024STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloudcited by 3
  5. 2024A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processorcited by 2
  6. 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
  7. 2024ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networkscited by 1
  8. 2024OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machinescited by 0
  9. 2025Quantum Ensembling Methods for Healthcare and Life Sciencecited by 0
  10. 2026Empirical Study of Observable Sets in Multiclass Quantum Classificationcited by 0
  11. 2026Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealingcited by 0