# Do We Really Need Quantum Machine Learning?: A Multidimensional Empirical Study

arXiv: https://arxiv.org/abs/2605.27923 · HTML: https://mlsanity.com/paper/2605.27923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum machine learning for image classification](https://mlsanity.com/paper/2304.09224/) (2023)
- [Systematic Literature Review: Quantum Machine Learning and its applications](https://mlsanity.com/paper/2201.04093/) (2022)
- [Entanglement-guided architectures of machine learning by quantum tensor network](https://mlsanity.com/paper/1803.09111/) (2018)
- [Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures](https://mlsanity.com/paper/2403.14772/) (2024)
- [Quantum Generative Adversarial Networks: Bridging Classical and Quantum Realms](https://mlsanity.com/paper/2312.09939/) (2023)
- [Analysis of Quantum Image Representations for Supervised Classification](https://mlsanity.com/paper/2507.22039/) (2025)
- [Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?](https://mlsanity.com/paper/2511.20710/) (2025)
- [Image Classification Based on Quantum KNN Algorithm](https://mlsanity.com/paper/1805.06260/) (2018)

## Built on by (most cited first, 1 of 1)

- [Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting](https://mlsanity.com/paper/2610.12148/) (2026)

## Abstract (arXiv)

The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm. This paper presents a comprehensive benchmarking study of classical and quantum machine learning models for image recognition on the MNIST handwritten digit dataset, evaluating both traditional models, a Classical Support Vector Machine (CSVM) and a Quantum Support Vector Machine (QSVM), and deep neural network models, a Classical Convolutional Neural Network (CCNN) and a Quantum Convolutional Neural Network (QCNN), across four performance dimensions: classification accuracy, computational runtime, parameter count, and memory requirements. Experiments are conducted as functions of both feature dimensionality and sample size, and across CPU and GPU execution environments, providing a controlled, multidimensional comparison to address gaps in prior work. For the SVM-based models, QSVM consistently outperforms CSVM in accuracy, reaching ~ 0.90 versus ~ 0.85 at 1,000 samples, with a higher computational cost. A feature count of 10 qubits and a sample size in the range of 200 -- 500 emerge as practical operating points that balance accuracy and runtime. For the neural network models, CCNN and QCNN achieve comparable classification accuracy, both exceeding 0.96 at 64 features and 60,000 samples, yet QCNN offers superior parameter and memory efficiency at higher feature counts, while incurring higher runtime. Across both model families, quantum models consistently outperform classical models by greater margins in accuracy as feature dimensionality or sample size increases.