Abstract: A method and system may utilize a quantum information state analog to reinforcement learning techniques to determine whether to adapt a course of treatment for an oncology patient. A quantum-based reinforcement learning engine may represent a decision to adapt and a decision not to adapt the course of treatment for the oncology patient as quantum information states in a superposition. Each quantum information state has a corresponding amplitude indicative of the likelihood that the quantum information state has a higher expected clinical outcome for the oncology patient. Using a quantum search algorithm, the quantum-based reinforcement learning engine identifies amplitudes for each quantum information state in the superposition. The quantum-based reinforcement learning engine instructs a health care provider to adapt the course of treatment for the oncology patient when a likelihood corresponding to the decision to adapt state exceeds a likelihood threshold.