Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Support Vector Regression for High-Dimensional Data: A Hybrid Quantum-Classical Approach

RamMohan Reddy Kundavaram, Rahul Reddy Bandhela, Abhishake Reddy Onteddu

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5414059

Related papers

Quantum-assisted support vector regression

Archismita Dalal, Mohsen Bagherimehrab, Barry C. Sanders

arXiv:2111.09304•Quantum Information Processing•Mar 10, 2025•5 citations

Quantum-Enhanced Support Vector Regression for Full-Duplex Self-Interference Cancellation

Mohamed Ibrahim Elsayed, Octavia A. Dobre

This letter proposes a quantum-enhanced support vector regression (QSVR) approach to cancel the self-interference in full-duplex transceivers. The proposed approach leverages quantum feature maps, incorporating quantum circuits, to encode classical data into a higher-dimensional feature space. Simulation results unveil a substantial performance enhancement by the proposed QSVR approach over the classical SVR and state-of-the-art hybrid quantum-classical neural network in the literature when being trained with a smaller set of training data.

IEEE Wireless Communications Letters•Jul 10, 2025•1 citation

PRICE AND SALES VOLUME FORECASTING USING QUANTUM SUPPORT VECTOR REGRESSION

Asliddin Sayidqulov, Fayzullo Makhmadiyarovich Nazarov

This article analyzes the issue of accurately predicting the price and sales volume of retail products based on classical and quantum approaches. The classical Support Vector Regression (SVR) model was evaluated in comparison with the Quantum Support Vector Regression (QSVR) model based on quantum computing. Latent representations of the data were obtained through dimensionality reduction using an autoencoder and utilized for forecasting. The experimental results demonstrated that the QSVR model outperforms the classical SVR model in terms of MAE, MSE, and R2 indicators. The study effectively employed quantum kernel functions, data re-uploading feature maps, and the COBYLA optimization algorithm. The findings confirm the practical potential of quantum machine learning methods.

International Bulletin of Applied Sciences and Technology•Aug 18, 2025•0 citations

Quantum kernel estimation-based quantum support vector regression

Xiaojian Zhou, Jieyao Yu, Junfan Tan +1 more

Quantum Information Processing•Jan 23, 2024•27 citations

Quantum Machine Learning Models for High-Dimensional Data Analysis

Ambuj Kumar Agarwal, Pradnya Sulas Borkar, Sumit Chafle +3 more

International Conference on Emerging Smart Computing and Informatics (ESCI)•Mar 11, 2026•0 citations

Quantum-Assisted Support Vector Regression for Detecting Facial Landmarks.

Archismita Dalal, Mohsen Bagherimehrab, Barry C. Sanders

The classical machine-learning model for support vector regression (SVR) is widely used for regression tasks, including weather prediction, stock-market and real-estate pricing. However, a practically realisable quantum version for SVR remains to be formulated. We devise annealing-based algorithms, namely simulated and quantum-classical hybrid, for training two SVR models, and compare their empirical performances against the SVR implementation of Python's scikit-learn package and the SVR-based state-of-the-art algorithm for the facial landmark detection (FLD) problem. Our method is to derive a quadratic-unconstrained-binary formulation for the optimisation problem used for training a SVR model and solve this problem using annealing. Using D-Wave's Hybrid Solver, we construct a quantum-assisted SVR model, thereby demonstrating a slight advantage over classical models regarding landmark-detection accuracy. Furthermore, we observe that annealing-based SVR models predict landmarks with lower variances compared to the SVR models trained by greedy optimisation procedures. Our work is a proof-of-concept example for applying quantu-assisted SVR to a supervised learning task with a small training dataset.

•Nov 17, 2021•2 citations
This letter proposes a quantum-enhanced support vector regression (QSVR) approach to cancel the self-interference in full-duplex transceivers. The proposed approach leverages quantum feature maps, incorporating quantum circuits, to encode classical data into a higher-dimensional feature space. Simulation results unveil a substantial performance enhancement by the proposed QSVR approach over the classical SVR and state-of-the-art hybrid quantum-classical neural network in the literature when being trained with a smaller set of training data.
This article analyzes the issue of accurately predicting the price and sales volume of retail products based on classical and quantum approaches. The classical Support Vector Regression (SVR) model was evaluated in comparison with the Quantum Support Vector Regression (QSVR) model based on quantum computing. Latent representations of the data were obtained through dimensionality reduction using an autoencoder and utilized for forecasting. The experimental results demonstrated that the QSVR model outperforms the classical SVR model in terms of MAE, MSE, and R2 indicators. The study effectively employed quantum kernel functions, data re-uploading feature maps, and the COBYLA optimization algorithm. The findings confirm the practical potential of quantum machine learning methods.
The classical machine-learning model for support vector regression (SVR) is widely used for regression tasks, including weather prediction, stock-market and real-estate pricing. However, a practically realisable quantum version for SVR remains to be formulated. We devise annealing-based algorithms, namely simulated and quantum-classical hybrid, for training two SVR models, and compare their empirical performances against the SVR implementation of Python's scikit-learn package and the SVR-based state-of-the-art algorithm for the facial landmark detection (FLD) problem. Our method is to derive a quadratic-unconstrained-binary formulation for the optimisation problem used for training a SVR model and solve this problem using annealing. Using D-Wave's Hybrid Solver, we construct a quantum-assisted SVR model, thereby demonstrating a slight advantage over classical models regarding landmark-detection accuracy. Furthermore, we observe that annealing-based SVR models predict landmarks with lower variances compared to the SVR models trained by greedy optimisation procedures. Our work is a proof-of-concept example for applying quantu-assisted SVR to a supervised learning task with a small training dataset.