ABSTRACT Continuous‐variable quantum‐inspired feature encodings have attracted increasing attention as structured transformations for hybrid and classical machine learning. Despite growing interest, their practical behavior within classical learning pipelines, particularly their interaction with different model architectures, remains insufficiently understood. In this work, we analyze three continuous‐variable quantum‐inspired feature encoding mechanisms, namely displacement, squeezing, and Instantaneous Quantum Polynomial style embeddings, when used as feature mappings for classical learning models. Using a real‐world telecommunications customer churn dataset, we examine how these encodings reshape feature geometry and interact with linear, distance‐based, and ensemble classifiers under varying levels of dimensionality reduction. The results indicate that smooth Gaussian encodings, such as displacement and squeezing, exhibit stable interactions with neighborhood‐based and ensemble models, while providing limited benefit for strictly linear classifiers. In contrast, Instantaneous Quantum Polynomial style embeddings show comparatively unstable behavior across several classical learning models, suggesting higher sensitivity to discretization and classifier inductive bias. Classical feature representations remain competitive in absolute predictive performance, indicating that the principal value of continuous‐variable quantum‐inspired encodings lies in their representational and feature transformation capabilities rather than in universal accuracy gains. Overall, the study highlights that encoding effectiveness depends strongly on compatibility between the transformed feature space and the downstream learning architecture, offering useful guidance for simulator‐based and hybrid machine learning settings.
ABSTRACT Continuous‐variable quantum‐inspired feature encodings have attracted increasing attention as structured transformations for hybrid and classical machine learning. Despite growing interest, their practical behavior within classical learning pipelines, particularly their interaction with different model architectures, remains insufficiently understood. In this work, we analyze three continuous‐variable quantum‐inspired feature encoding mechanisms, namely displacement, squeezing, and Instantaneous Quantum Polynomial style embeddings, when used as feature mappings for classical learning models. Using a real‐world telecommunications customer churn dataset, we examine how these encodings reshape feature geometry and interact with linear, distance‐based, and ensemble classifiers under varying levels of dimensionality reduction. The results indicate that smooth Gaussian encodings, such as displacement and squeezing, exhibit stable interactions with neighborhood‐based and ensemble models, while providing limited benefit for strictly linear classifiers. In contrast, Instantaneous Quantum Polynomial style embeddings show comparatively unstable behavior across several classical learning models, suggesting higher sensitivity to discretization and classifier inductive bias. Classical feature representations remain competitive in absolute predictive performance, indicating that the principal value of continuous‐variable quantum‐inspired encodings lies in their representational and feature transformation capabilities rather than in universal accuracy gains. Overall, the study highlights that encoding effectiveness depends strongly on compatibility between the transformed feature space and the downstream learning architecture, offering useful guidance for simulator‐based and hybrid machine learning settings.
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.
Quantum Machine Learning (QML) has a theoretical advantage over classical algorithms for specific synthetic data that is computationally hard. However, it remains unclear whether such theoretical advantages play a meaningful role for realistic classical data, such as natural images. Quantum structural properties induced by unitary operations or entanglement introduce inductive biases that differ from those of classical models, potentially leading to distinct learning behavior on real-world data. In a fully quantum learning pipeline, classical real-world data are first encoded into quantum states via a quantum feature mapping, analogous to a classical feature extractor, and a quantum classifier then classifies the resulting states. Quantum feature mappings generate representations that are difficult to realize classically, while also inducing representation-level biases that do not necessarily match the structure of realistic data. To enable a fair comparison at the classifier level, we focus on the classifier and use classical feature extractors that already provide powerful representations for natural images. By controlling the data scale and supervision strength, we analyze how the unitary structure of quantum classifiers affects generalization, and show that their effectiveness depends strongly on the geometry of the input representations. These results indicate that quantum classifiers benefit from classical features that are aligned with quantum feature spaces. Our analysis shows that kernel alignment identifies representations compatible with quantum feature spaces, providing insights into translating classical models into quantum algorithms.
Quantum machine learning is often motivated by the idea that quantum systems can expose useful high-dimensional structure that is difficult to access with classical models. We isolate one central component of this claim: the fixed data-encoding map. Amplitude, angle, and basis encoding are evaluated as deterministic feature maps for classical supervised learning under matched output dimensionality and strong classical controls. The benchmark compares these encodings against raw linear models, random Fourier features, polynomial features, PCA, RBF SVMs, and shallow neural networks across diverse classical datasets. Rather than treating performance as a single endpoint, we analyze the geometry of each representation through effective rank, condition number, centered kernel alignment, predictive performance, and practical overhead. The resulting picture is mechanistic: amplitude encoding can remove magnitude information through unit-sphere normalization, angle encoding can become geometrically redundant with raw linear features, and basis encoding can impose a binary Hamming geometry that is poorly aligned with smooth decision structure. These findings do not argue against quantum computation, however, they show that fixed quantum-inspired encoding geometry alone is not a reliable source of machine-learning advantage on classical data.
Quantum computing has inspired new ways to represent and process data, offering perspectives that can also benefit classical machine learning. In this work, we investigate how quantum-inspired data representations can be integrated into classical machine learning classifiers such as Logistic Regression, Support Vector Machines, and Naive Bayes for binary classification tasks. The proposed approach utilizes a simple amplitude-based encoding that normalizes feature vectors and incorporates phase-like transformations, assisting classical models to capture complex interactions and patterns in the data similar to quantum information processing. We evaluate this approach on both synthetic and three other benchmark datasets, comparing standard and quantum-inspired data representations. The results highlights that the quantum-inspired data encoding often matches or improves classifier performance in terms of accuracy, F1-Score, and precision. Additionally, the normalized encoding provides a more structured representation of the data, which can help with interpretability and visualization. These results highlight the potential of quantum-inspired transformations as a practical and effective tool for improving classical machine learning pipelines without requirement of quantum hardware.
This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and 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 to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on 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.
Quantum-enhanced methods have recently attracted significant attention for addressing a wide range of machine learning problems. Many approaches exploit the hybrid resources of noisy quantum devices in combination with classical computing, particularly for supervised learning. Among these, the variational quantum classifier (VQC) offers potential quantum advantage by mapping classical data into high-dimensional quantum feature spaces that are difficult to simulate classically. While several quantum data-encoding methods have been explored, the efficient mapping of discrete attributes—such as postal codes, age groups, or categorical identifiers—has received limited attention, despite their importance in real-world classification tasks. In this work, we introduce an efficient encoding strategy based on Quantum Random Access Codes (QRACs) for embedding discrete features into compact quantum representations. QRACs enable multiple bits of information to be encoded into fewer qubits, thereby reducing resource requirements while preserving feature separability. We present several QRAC-based schemes, analyze their theoretical properties, and evaluate their performance through algebraic simulations. Empirical studies on real-world datasets, implemented on both simulators and quantum hardware, demonstrate that QRA-Cenhanced VQCs achieve faster training, reduced parameter counts, and competitive classification accuracy. These results highlight QRACs as a practical tool for advancing the applicability of quantum machine learning to structured datasets.