Quantum Markov Modeling for Healthcare
Abstract
A Markov model defines a system of states, composed by the feasible transition paths between those states, and the parameters of those transitions. The paths and parameters may be a representative way to address healthcare issues, such as to identify the most likely sequence of patient health states given the sequence of observations. Furthermore estimating the length of stay (LoS) of patients in hospitalization is one of the challenges that Markov models allow us to solve. However, finding the maximum probability of any path that gets to state at time t, can have high computational cost. A quantum approach allows us to take advantage of quantum computation since the calculated probabilities can be in several states, ending up to outperform classical computing due to the possible superposition of states when handling large amounts of data. The aid of quantum physics-based architectures and machine learning techniques are therefore appropriated to address the complexity of healthcare.
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