Quantum computing for radiation therapy optimization.

Area of Science:

• Quantum Computing
• Medical Physics
• Computational Optimization

Background:

• Classical optimization methods struggle with complex, large-scale radiotherapy planning problems.
• Quantum computing (QC) presents a novel solution for these challenges.
• Biomedical applications are increasingly leveraging QC for complex optimization tasks.

Purpose of the Study:

• To implement and evaluate quantum annealing and QAOA for radiotherapy planning optimization.
• To utilize an Ising Hamiltonian formulation for the cost function.
• To perform the first empirical implementation on circuit-based quantum hardware for this application.

Main Methods:

• Formulated a simplified radiotherapy optimization problem.
• Solved using quantum annealing on D-Wave and QAOA on IBM Quantum hardware.
• Tested Hamiltonian formulation on proof-of-principle and clinically relevant prostate proton plans.
• Evaluated one- and two-qubits-per-voxel encodings for scalability.

Main Results:

• First demonstration of QC circuit-model hardware for radiotherapy planning optimization.
• Quantum annealing successfully found optimal solutions.
• Successful application to a realistic bilateral prostate proton plan using clinical data.
• Demonstrated feasibility of QC optimization with clinically sourced parameters.

Conclusions:

• Quantum optimization shows potential advantages over classical methods in radiotherapy.
• Hamiltonian formulation and validation on real hardware are key steps for QC-based treatment planning.
• Further research needed on scalability and overcoming practical challenges for clinical integration.