# Classification of the Fashion-MNIST Dataset on a Quantum Computer

arXiv: https://arxiv.org/abs/2403.02405 · HTML: https://mlsanity.com/paper/2403.02405/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [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-Inspired Tensor Networks](https://mlsanity.com/paper/1605.05775/) (2016)
- [Digital-analog quantum learning on Rydberg atom arrays](https://mlsanity.com/paper/2401.02940/) (2024)

## Built on by (most cited first, 9 of 9)

- [Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers](https://mlsanity.com/paper/2407.14055/) (2024)
- [Quantum Deep Equilibrium Models](https://mlsanity.com/paper/2410.23940/) (2024)
- [Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning](https://mlsanity.com/paper/2508.00024/) (2025)
- [Quantum feature-map learning with reduced resource overhead](https://mlsanity.com/paper/2510.03389/) (2025)
- [Intrinsic preservation of plasticity in continual quantum learning](https://mlsanity.com/paper/2511.17228/) (2025)
- [Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation](https://mlsanity.com/paper/2603.00233/) (2026)
- [Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?](https://mlsanity.com/paper/2409.01496/) (2024)
- [Hybrid quantum tensor networks for aeroelastic applications](https://mlsanity.com/paper/2508.05169/) (2025)
- [Fisher Information, Training and Bias in Fourier Regression Models](https://mlsanity.com/paper/2510.06945/) (2025)

## Abstract (arXiv)

The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question. Conventional methods for encoding classical data into quantum computers are not only too costly for a potential quantum advantage in the algorithms but also severely limit the scale of feasible experiments on current hardware. Therefore, recent works, despite claiming the near-term suitability of their algorithms, do not provide experimental benchmarking on standard machine learning datasets. We attempt to solve the data encoding problem by improving a recently proposed variational algorithm [1] that approximately prepares the encoded data, using asymptotically shallow circuits that fit the native gate set and topology of currently available quantum computers. We apply the improved algorithm to encode the Fashion-MNIST dataset [2], which can be directly used in future empirical studies of quantum machine learning algorithms. We deploy simple quantum variational classifiers trained on the encoded dataset on a current quantum computer ibmq-kolkata [3] and achieve moderate accuracies, providing a proof of concept for the near-term usability of our data encoding method.