论文标题
量子辍学：在量子近似优化算法的硬度上
Quantum Dropout: On and Over the Hardness of Quantum Approximate Optimization Algorithm
论文作者
论文摘要
在能量景观崎rug的情况下，组合优化问题变得非常困难，并且全球最小值位于配置空间的狭窄区域。当使用量子近似优化算法（QAOA）来解决这些困难情况时，我们发现困难主要源自QAOA量子电路而不是成本函数。为了减轻问题，我们选择性地辍学了定义量子电路的子句，同时保持成本功能完整。由于优化问题的组合性质，电路中的子句的辍学不会影响解决方案。我们的数值结果证实了QAOA的性能改善，并通过各种类型的量子抛弃实现。
A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and the global minimum locates in a narrow region of the configuration space. When using the quantum approximate optimization algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from the QAOA quantum circuit instead of the cost function. To alleviate the issue, we selectively dropout the clauses defining the quantum circuit while keeping the cost function intact. Due to the combinatorial nature of the optimization problems, the dropout of clauses in the circuit does not affect the solution. Our numerical results confirm improvements in QAOA's performance with various types of quantum-dropout implementation.
