SU‐D‐BRB‐05: Quantum Learning for Knowledge‐Based Response‐Adaptive Radiotherapy

Abstract

Purpose:There is tremendous excitement in radiotherapy about applying data‐driven methods to develop personalized clinical decisions for real‐time response‐based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics‐inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient's characteristics and optimize outcomes.Methods:We studied 88 liver SBRT patients with 35 on non‐adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split‐course of 3+2 fractions with a month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5‐variable state represented by patient's clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision‐making to adapt at one month was considered. The MDP objective was defined by the complication‐free tumor control (P+=TCPx(1‐NTCP)). A simple regression model represented state‐action mapping. Single bit in classical MDP and a qubit of 2‐superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q‐learning and quantum searching was performed using Grover's algorithm, which applies uniform superposition over possible states and yields quadratic speed‐up.Results:Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous‐courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the quantum approach averaged a more optimistic 0.57±0.4, but with high phase fluctuations.Conclusion:Our results demonstrate that quantum machine learning approaches provide a feasible and promising framework for real‐time and sequential clinical decision‐making in adaptive radiotherapy.