Title : Quantum-assisted optimization and simulation for orthopedic treatment planning
Abstract:
Orthopedic treatment planning involves high-dimensional, non-linear biomechanical simulations that require precise modelling of bone-tissue interactions, prosthetic placement, and rehabilitation trajectories. We propose a Quantum-Assisted Optimization and Simulation Framework (QAOSF) that combines variational quantum algorithms, hybrid solvers, and multi- scale modelling for personalized orthopedic interventions. Where (u) is the stress tensor, the surface normal, are Lamé parameters, andepresents damping in tissue. To optimize implant positioning, load balancing, and joint mobility constraints, we formulate a quantum variational problem: Where is a cost Hamiltonian encoding objective such as minimizing shear stress, maximizing mobility are constraint operators (e.g., anatomical boundaries). Parameterized quantum state from a variational quantum circuit. atient-specific modeling is enhanced using Quantum Gaussian Process Regression (QGPR) for prediction of postoperative outcomes: For morphable implants and dynamic stabilizers, we solve: where is the mass matrix, is potential energy due to joint stiffness and muscular esistance, are nonlinear constraints representing tissue tolerance and anatomical bounds.The QAOSF framework enables adaptive orthopedic planning through real-time simulation, quantum- enhanced parameter tuning, and biomechanical prediction, ensuring efficient, accurate, and personalized treatments. It integrates quantum variational circuits with mechanical models, using q- FEA and qSVR to predict surgical outcomes. Leveraging entangled quantum states, it models anatomical uncertainty. Initial results with IBM Qiskit and D-Wave show improved accuracy and faster convergence over classical methods.
