# Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance

arXiv: https://arxiv.org/abs/2504.06497 · HTML: https://mlsanity.com/paper/2504.06497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [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)
- [Quantum computing overview: discrete vs. continuous variable models](https://mlsanity.com/paper/2206.07246/) (2022)
- [Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector](https://mlsanity.com/paper/2311.10799/) (2023)
- [Quantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing](https://mlsanity.com/paper/2311.10363/) (2023)
- [Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis](https://mlsanity.com/paper/2502.07806/) (2025)

## Abstract (arXiv)

This study explores the intersection of continuous-variable quantum computing (CVQC) and classical machine learning, focusing on CVQC data encoding techniques, including Displacement encoding and squeezing encoding, alongside Instantaneous Quantum Polynomial (IQP) encoding from discrete quantum computing. We perform an extensive empirical analysis to assess the impact of these encoding methods on classical machine learning models, such as Logistic Regression, Support Vector Machines, K-Nearest Neighbors, and ensemble methods like Random Forest and LightGBM. Our findings indicate that CVQC-based encoding methods significantly enhance feature expressivity, resulting in improved classification accuracy and F1 scores, especially in high-dimensional and complex datasets. However, these improvements come with varying computational costs, which depend on the complexity of the encoding and the architecture of the machine learning models. Additionally, we examine the trade-off between quantum expressibility and classical learnability, offering valuable insights into the practical feasibility of incorporating these quantum encodings into real-world applications. This study contributes to the growing body of research on quantum-classical hybrid learning, emphasizing the role of CVQC in advancing quantum data representation and its integration into classical machine learning workflows.