Quantum-Inspired Control Systems for Collaborative Robots in Precision Healthcare Delivery
Abstract
Precision healthcare increasingly relies on robotic platforms that must operate safely, adaptively, and transparently in close proximity to patients and clinical staff. This paper develops a comprehensive research agenda and technical framework for quantum-inspired control systems (QICS) applied to collaborative robots (cobots) in precision healthcare delivery. We define quantum-inspired methods as classical algorithms and control architectures that borrow mathematical ideas, optimization primitives, and stochastic dynamics from quantum computing and quantum control (for example, annealing-style heuristics, simulated bifurcation, tensor-network encodings, and quantum-inspired variational optimization), but which execute on classical or hybrid hardware. After surveying the state of the art in quantum-inspired computation and healthcare robotics, we propose (1) a formal problem statement that captures high-fidelity clinical constraints (safety, latency, interpretability, clinical outcome metrics), (2) mathematically specified QICS architectures for perception, task allocation, trajectory optimization, and closed-loop manipulation, and (3) rigorous evaluation protocols (simulation, hardware-in-the-loop, and human factors testing) suitable for translational research. We illustrate the approach through three precision-healthcase use cases robotic microsurgery assistance, adaptive drug delivery via robotically assisted infusion, and rehabilitation cobots for motor retraining…
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