Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology

TL;DR

This paper applies quantum-inspired genetic algorithms to optimize patient scheduling in proton therapy, demonstrating faster convergence and better population efficiency compared to classical methods, with potential for future quantum computing implementation.

Contribution

It introduces a quantum-inspired genetic algorithm tailored for radiation oncology scheduling, showing improved population efficiency and convergence over classical algorithms.

Findings

Quantum-inspired algorithms outperform classical in population size efficiency.

The approach converges reliably to clinically feasible schedules.

Long run times for large cases highlight the need for true quantum computation.

Abstract

Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-fitness convergence and small resource requirement. Here the application to the patient scheduling problem in proton therapy is reported. Quantum chromosomes are tailored to possess the superposed data of patient IDs and gantry statuses. Selection and repair strategies are also elaborated for reliable convergence to a clinically feasible schedule although the employed model is not complex. Clear advantage in population size is shown over the classical counterpart in our numerical results for both a medium-size test case and a large-size practical problem instance. It is, however, observed that program run time is rather long for the large-size practical case, which is due to the limitation of classical emulation and demands the forthcoming true quantum computation. Our results also revalidate the stability of the conventional classical genetic algorithm.