# Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracy

arXiv: https://arxiv.org/abs/2311.10375 · HTML: https://mlsanity.com/paper/2311.10375/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)

## Built on by (most cited first, 30 of 39)

- [ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks](https://mlsanity.com/paper/2503.21815/) (2025)
- [Feature Importance and Explainability in Quantum Machine Learning](https://mlsanity.com/paper/2405.08917/) (2024)
- [Predictive Performance of Deep Quantum Data Re-uploading Models](https://mlsanity.com/paper/2505.20337/) (2025)
- [Quantum Long Short-Term Memory for Drug Discovery](https://mlsanity.com/paper/2407.19852/) (2024)
- [Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series](https://mlsanity.com/paper/2505.11631/) (2025)
- [Barren plateaus are amplified by the dimension of qudits](https://mlsanity.com/paper/2405.08190/) (2024)
- [Comparing Quantum Encoding Techniques](https://mlsanity.com/paper/2410.09121/) (2024)
- [Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions](https://mlsanity.com/paper/2501.15828/) (2025)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Quantum State Preparation with the QNN-based SRBB Algorithm](https://mlsanity.com/paper/2503.13647/) (2025)
- [Benchmarking data encoding methods in Quantum Machine Learning](https://mlsanity.com/paper/2505.14295/) (2025)
- [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)
- [Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification](https://mlsanity.com/paper/2512.02066/) (2025)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Quantum Algorithms: A New Frontier in Financial Crime Prevention](https://mlsanity.com/paper/2403.18322/) (2024)
- [Reverse Map Projections as Equivariant Quantum Embeddings](https://mlsanity.com/paper/2407.19906/) (2024)
- [Training quantum machine learning models on cloud without uploading the data](https://mlsanity.com/paper/2409.04602/) (2024)
- [Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance](https://mlsanity.com/paper/2504.06497/) (2025)
- [An Efficient Quantum Classifier Based on Hamiltonian Representations](https://mlsanity.com/paper/2504.10542/) (2025)
- [Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications](https://mlsanity.com/paper/2505.24765/) (2025)
- [Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations](https://mlsanity.com/paper/2507.00019/) (2025)
- [Quantum Neural Network applications to Protein Binding Affinity Predictions](https://mlsanity.com/paper/2508.03446/) (2025)
- [Entanglement Detection with Quantum-inspired Kernels and SVMs](https://mlsanity.com/paper/2508.17909/) (2025)
- [Histogram Driven Amplitude Embedding for Qubit Efficient Quantum Image Compression](https://mlsanity.com/paper/2509.04849/) (2025)
- [Triplet Loss Based Quantum Encoding for Class Separability](https://mlsanity.com/paper/2509.15705/) (2025)
- [Re-uploading quantum data: a universal function approximator for quantum inputs](https://mlsanity.com/paper/2509.18530/) (2025)
- [Comprehensive Analysis of VQC for Financial Fraud Detection: A Comparative Study of Quantum Encoding Techniques and Architectural Optimizations](https://mlsanity.com/paper/2509.25245/) (2025)
- [Adaptive Conformal Prediction for Quantum Machine Learning](https://mlsanity.com/paper/2511.18225/) (2025)
- [Formal Verification of Noisy Quantum Reinforcement Learning Policies](https://mlsanity.com/paper/2512.01502/) (2025)
- [Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization](https://mlsanity.com/paper/2512.14181/) (2025)

## Abstract (arXiv)

This research explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms, aiming to assess the performance enhancements and computational implications across a spectrum of models. We explore various classical-to-quantum mapping methods, ranging from basis encoding, angle encoding to amplitude encoding for encoding classical data, we conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead. This study underscores the potential of quantum data embedding in enhancing classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research directions may involve refining quantum encoding processes to optimize computational efficiency and exploring scalability for real-world applications. Our work contributes to the growing body of knowledge at the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.