Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performance
Minati Rath, Hema Date
TL;DR
This work addresses how continuous-variable quantum data encodings can augment classical machine learning performance by mapping real-valued data into quantum feature spaces. It evaluates three encoding schemes—$IQP$, Displacement, and Squeezing—within a hybrid quantum–classical pipeline implemented in $PennyLane$ on a telecom churn dataset, using PCA to manage dimensionality. Results show $IQP$ underperforms relative to Displacement and Squeezing, which achieve higher accuracy and F1 across multiple classifiers, with $\text{PCA}=23$ often yielding the best trade-off between expressivity and efficiency; nevertheless, quantum embeddings impose substantial runtimes. Overall, the study provides practical guidance on when quantum-inspired encodings may offer benefits in real-world predictive tasks and highlights important directions for optimizing hybrid quantum–classical learning systems.$
Abstract
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.