Green Quantum Architectures for Energy-Efficient Surgical AI
Abstract
The rapid evolution of autonomous surgical robotics necessitates intelligent decision-making systems capable of operating under strict real-time, safety-critical, and energy-constrained environments. Contemporary surgical AI predominantly relies on large-scale deep learning models deployed on power-intensive computing platforms, raising concerns regarding sustainability and environmental impact. This chapter introduces the concept of Green Quantum Architectures, an emerging interdisciplinary framework integrating quantum computing, energy-aware system design, and surgical artificial intelligence to enable sustainable and high-precision autonomous robotic surgery. The chapter examines how quantum-enhanced computation, hybrid quantum classical learning models, and energy-efficient hardware–software co-design can significantly reduce computational overhead and carbon footprint. A unified architectural vision is presented, wherein quantum machine learning accelerates complex optimization tasks such as surgical path planning, uncertainty modeling.
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