Quantum computing for radiation therapy optimization
Abstract
Background: Quantum computing (QC) is emerging as a transformative tool for solving complex optimization problems across various fields, including biomedical applications. While classical optimization methods are well-established, they frequently face limitations when applied to complex and large-scale problems in radiotherapy planning.
Purpose: This study aims to explore the implementation and evaluate the effectiveness of quantum optimization methods, specifically quantum annealing and the quantum approximate optimization algorithm (QAOA), in radiotherapy planning. In particular, we employ an Ising Hamiltonian formulation of the cost function, empirically implementing it on annealing-based quantum hardware and for the first time on circuit-based one.
Methods: We formulated a simplified radiotherapy optimization problem and solved it using quantum annealing on a D-Wave quantum annealer. Subsequently, we adapted this optimization problem for the QAOA framework and implemented it on IBM Quantum circuit-model hardware. Comparative analyses were conducted between classical and quantum methods and implementations, highlighting QC's potential advantages and limitations in specific optimization contexts. To demonstrate that the Hamiltonian formulation is valid and practically usable, we first tested it in simplified proof-of-principle examples and then extended it to a more clinically relevant bilateral prostate proton plan. In this new example, dose parameters were extracted…
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