Technology Category
Planning, Routing, and Scheduling: SchedulingSearch and Optimization: Mixed Discrete/Continuous SearchMachine Learning: Quantum Machine Learning
Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
🤖 AI Summary
To address patient scheduling difficulties and low resource utilization in proton therapy, this paper proposes a quantum-inspired genetic algorithm (QIGA). Methodologically, it introduces the first quantum-chromosome encoding tailored for radiotherapy scheduling, where qubit superposition simultaneously encodes patient IDs and gantry states, and incorporates an adaptive repair operator to ensure clinical constraint satisfaction. The contributions are threefold: (1) significantly reduced population size—by 42% for medium-scale and 38% for large-scale instances—while improving convergence accuracy and guaranteeing 100% adherence to hard clinical constraints; (2) first empirical validation of quantum-inspired optimization’s feasibility and robustness in real-world radiotherapy scheduling; and (3) demonstration that quantum-state advantages can be efficiently simulated on classical hardware, though runtime bottlenecks highlight the necessity of future integration with genuine quantum hardware.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Optimizing patient scheduling in proton therapy
Applying quantum-inspired genetic algorithms for fast convergence
Addressing resource limitations in large-scale practical cases
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum-inspired genetic algorithm for scheduling
Tailored quantum chromosomes with superposed data
Selection and repair strategies for reliable convergence
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