Abstract

Hybrid quantum-classical algorithms have emerged as a powerful computational paradigm, offering significant advancements in solving complex optimization problems in artificial intelligence (AI). This book chapter explores the potential of hybrid approaches to overcome the limitations of classical optimization algorithms by harnessing the capabilities of quantum computing. A detailed examination of scalability challenges, integration strategies, and the dynamic adjustment of hybrid algorithms based on problem characteristics was presented. Additionally, the chapter discusses the role of quantum hardware advancements, error correction techniques, and the impact of alternative quantum algorithms in shaping the future of AI optimization. By addressing the interdisciplinary applications of these hybrid approaches, particularly in environmental modeling and climate change mitigation, the chapter outlines new research opportunities and trends that could drive the next wave of innovation in AI-driven optimization.

Introduction

Hybrid quantum-classical algorithms represent a promising frontier in the field of artificial intelligence (AI) optimization, combining the strengths of classical computing and emerging quantum technologies to solve complex problems that are difficult or impossible for classical algorithms alone [1,2]. Classical optimization methods, such as gradient descent or evolutionary algorithms, have long been effective in solving a wide range of problems; they face significant limitations when applied to problems with vast solution spaces or non-linear relationships [3,4,5]. Quantum computing, with its ability to process information in parallel and explore multiple solutions simultaneously, offers new possibilities for optimization, yet current quantum computers are still in their infancy and face practical constraints such as limited qubit coherence times and susceptibility to noise [6,7,8]. Hybrid quantum-classical algorithms address these challenges by blending classical and quantum methods, enabling the exploration of optimization problems with greater efficiency and scalability [9].

One of the most compelling reasons for integrating quantum and classical approaches was the potential for overcoming the scalability challenges that classical optimization techniques encounter [10]. As the size and complexity of datasets and optimization problems grow, classical algorithms often struggle to maintain efficiency, especially in high-dimensional spaces [11]. Hybrid algorithms, by leveraging quantum computing’s potential for parallelism, can explore vast solution spaces more effectively. In particular, quantum subroutines, such as quantum annealing and variational quantum eigensolvers (VQE), can be embedded within classical optimization frameworks to accelerate the search for optimal solutions [12]. This integration allows for a more efficient division of labor, where classical computing handles data pre-processing and post-processing, while quantum resources are utilized for the most computationally intensive parts of the optimization process [13].