Quantum-Enhanced Machine Learning for Real-Time Ad Serving

Abstract

This paper presents a groundbreaking approach to addressing the growing computational challenges in real-time ad serving by leveraging quantum computing to accelerate machine learning (ML) algorithms. We propose a hybrid framework, the Quantum AdServer, which utilizes quantum algorithms alongside classical computing to reduce the time complexity of critical ML tasks in programmatic advertising. We explore both Variational Quantum Circuits (VQC) for near-term implementation on noisy intermediate-scale quantum (NISQ) devices and the Harrow-Hassidim-Lloyd (HHL) algorithm for future scenarios where more advanced quantum hardware is available. Our approach demonstrates significant improvements in both speed and scalability of personalized ad delivery, potentially revolutionizing the field of computational advertising. Through comprehensive theoretical analysis, simulations, and a detailed comparison of quantum methods, we showcase the potential of quantum-enhanced ML in ad tech while discussing practical challenges, including current hardware limitations and integration with existing ad-serving systems.