Quantum approximate optimization algorithm for Bayesian network structure learning

Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently, quantum technologies offer wide range advantages can be exploited to solve optimization tasks cannot addressed efficient way when utilizing classic computing approaches. In this work, specific type variational algorithm, the approximate was used problem, employing $$3n(n-1)/2$$ qubits, where n nodes learned. Our results showed algorithm approach offers competitive with state-of-the-art methods and quantitative resilience noise. The applied cancer benchmark justified use algorithms for solving problem.