Quantum-Assisted Support Vector Regression for Detecting Facial Landmarks

• URL: http://arxiv.org/abs/2111.09304v1
• Date: Wed, 17 Nov 2021 18:57:10 GMT
• Title: Quantum-Assisted Support Vector Regression for Detecting Facial Landmarks
• Authors: Archismita Dalal, Mohsen Bagherimehrab and Barry C. Sanders
• Abstract summary: We devise algorithms, namely simulated and quantum-classical hybrid, for training two SVR models. We compare their empirical performances against the SVR implementation of Python's scikit-learn package. Our work is a proof-of-concept example for applying quantu-assisted SVR to a supervised learning task.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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