# Quantum Approximate Optimization Algorithm for Bayesian network structure learning

arXiv: https://arxiv.org/abs/2203.02400 · HTML: https://mlsanity.com/paper/2203.02400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 51 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [Bayesian Network Structure Learning Using Quantum Annealing](https://mlsanity.com/paper/1407.3897/) (2014)
- [Evaluating Quantum Approximate Optimization Algorithm: A Case Study](https://mlsanity.com/paper/1910.04881/) (2019)
- [Autoregressive Asymmetric Linear Gaussian Hidden Markov Models](https://mlsanity.com/paper/2010.15604/) (2020)

## Abstract (arXiv)

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