Quantum-Ready AI for Autonomous Surgical Robotics

Classical artificial intelligence (AI) has transformed robotic surgery over the past decade, yet its computational foundations limit optimisation depth, data efficiency, and real-time responsiveness under intraoperative uncertainty. This chapter argues that quantum computing—through variational quantum algorithms (VQA), quantum machine learning (QML), and quantum deep reinforcement learning (QDRL)—offers a route beyond these limits when embedded in hybrid quantum-classical architectures. The discussion examines how these paradigms enhance surgical perception, preoperative planning, instrument recognition, adaptive robotic control, and clinical decision support, focusing on quantum convolutional neural networks (QCNN) and noisy intermediate-scale quantum (NISQ) algorithms as the near-term implementation substrate. Hardware noise, qubit decoherence, latency constraints, and regulatory readiness are critically examined alongside the pathway to fault-tolerant systems. A phased roadmap for clinical integration is proposed, centred on safety, explainability, and validation.

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