Overview
Sima is a PhD Candidate and quantum computing researcher with experience in financial industry in the intersection of quantum and future computing with optimization and machine learning. With a background in engineering and math and passion for art and science, she finds convergence science and interdisciplinary fields fascinating.
Information
“Quantum Bayesian Networks, construction, prediction, and inference”
Probabilistic graphical models such as Bayesian networks are widely used to model
stochastic systems to perform prediction, risk analysis, and system health monitoring,
which can become computationally expensive in large-scale systems.
We developed a systematic method for loading Bayesian networks into quantum circuits. An efficient quantum representation of Bayesian Networks can facilitate the application of other quantum algorithms, for performing inference or prediction, for instance. We then further expanded our approach from Generic Quantum BNs to Dynamic Quantum Bayesian Networks (DQBN) for representing time dependent systems.
Quantitative Research; Uncertainty Modeling; Statistical Analysis; Quantum computing; Optimization; Machine Learning, and Data Analysis
Quantum Information, Quantum algorithms, Probability and Statistics