Modeling Musical Knowledge With Quantum Bayesian Networks

Music is a multifaceted art form that requires a nuanced and comprehensive framework for analysis. This framework should encompass correlations among diverse musical attributes, including melody, harmony, rhythm, timbre, and dynamics. However, modeling the interaction between these attributes is anticipated to require substantial computational resources. In this study, we propose a Bayesian model to represent various musical attributes and explore its implementation and inference on a quantum computer. We show how to translate the Bayesian model to a quantum circuit and perform inference using the quantum rejection sampling algorithm on a quantum simulator. Through empirical evaluation utilizing a realworld dataset, we compare the performance of the quantum algorithm against classical rejection sampling. Our results reveal that the quantum method improves the acceptance rate of generated samples, thereby enhancing posterior probability estimates.