Abstract Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the quantum approximate optimization algorithm. We also demonstrate the experimental feasibility of our algorithms on a Quantinuum trapped-ion quantum processor powered by Honeywell.

中文翻译：

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过滤变分量子算法以进行组合优化

摘要如果算法有效地使用量子硬件，当前基于门的量子计算机有可能提供计算优势。为了使组合优化更有效，我们引入了滤波变分量子特征求解器，它利用滤波算子来实现更快、更可靠地收敛到最优解。此外，我们探索使用因果锥来减少量子计算机所需的量子比特数量。使用随机加权 MaxCut 问题，我们对我们的方法进行了数值分析，并表明它们的性能优于原始 VQE 算法和量子近似优化算法。我们还展示了我们的算法在由霍尼韦尔提供支持的 Quantinuum 俘获离子量子处理器上的实验可行性。