论文标题
贝叶斯网络结构学习的量子近似优化算法
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
论文作者
论文摘要
贝叶斯网络结构学习是一个NP硬性问题，最近几十年来许多传统方法面临。当前，量子技术提供了广泛的优势，可以利用这些优化任务来解决优化任务，这些任务在使用经典计算方法时无法有效地解决。在这项工作中，使用使用$ 3N（N-1）/2 $ Qubits的特定类型的变分量子算法（量子近似优化算法）来解决贝叶斯网络结构学习问题，其中$ n $是贝叶斯网络中的节点数量。我们的结果表明，量子近似优化算法方法通过最先进的方法和对量子噪声的定量弹性提供了竞争结果。该方法应用于癌症基准问题，结果证明使用变分量子算法解决贝叶斯网络结构学习问题是合理的。
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.
