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

arXiv: https://arxiv.org/abs/2309.15056 · HTML: https://mlsanity.com/paper/2309.15056/

## 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 (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective](https://mlsanity.com/paper/1906.11081/) (2019)
- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [VSQL: Variational Shadow Quantum Learning for Classification](https://mlsanity.com/paper/2012.08288/) (2020)
- [Image classification using quantum inference on the D-Wave 2X](https://mlsanity.com/paper/1905.13215/) (2019)
- [Quantum Speedup in Adaptive Boosting of Binary Classification](https://mlsanity.com/paper/1902.00869/) (2019)
- [Binary Classifier Inspired by Quantum Theory](https://mlsanity.com/paper/1903.01167/) (2019)

## Built on by (most cited first, 11 of 11)

- ['Eliv'agar: Efficient Quantum Circuit Search for Classification](https://mlsanity.com/paper/2401.09393/) (2024)
- [Toward Automated Quantum Variational Machine Learning](https://mlsanity.com/paper/2312.01567/) (2023)
- [Multi-Class Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2404.12741/) (2024)
- [STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud](https://mlsanity.com/paper/2405.18746/) (2024)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data](https://mlsanity.com/paper/2503.14473/) (2025)
- [ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks](https://mlsanity.com/paper/2408.13389/) (2024)
- [OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines](https://mlsanity.com/paper/2409.19823/) (2024)
- [Quantum Ensembling Methods for Healthcare and Life Science](https://mlsanity.com/paper/2506.02213/) (2025)
- [Empirical Study of Observable Sets in Multiclass Quantum Classification](https://mlsanity.com/paper/2602.08485/) (2026)
- [Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing](https://mlsanity.com/paper/2603.02958/) (2026)

## 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.