# Comparing Quantum Encoding Techniques

arXiv: https://arxiv.org/abs/2410.09121 · HTML: https://mlsanity.com/paper/2410.09121/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 11 Oct 2024.

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

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy](https://mlsanity.com/paper/2311.10375/) (2023)
- [Variational Quanvolutional Neural Networks with enhanced image encoding](https://mlsanity.com/paper/2106.07327/) (2021)
- [A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices](https://mlsanity.com/paper/2307.09771/) (2023)

## Built on by (most cited first, 2 of 2)

- [Quantum Model Parallelism for MRI-Based Classification of Alzheimer's Disease Stages](https://mlsanity.com/paper/2602.00128/) (2026)
- [Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding](https://mlsanity.com/paper/2602.16266/) (2026)

## Abstract (arXiv)

As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.