混合器旋转轴的自由度提高了量子近似优化算法的性能
Freedom of mixer rotation-axis improves performance in the quantum approximate optimization algorithm
摘要：
变分量子算法，如量子近似优化算法（QAOA）是在近期量子处理器上实现的特别有吸引力的候选者。由于诸如错误和量子位连接性等硬件现实将在不久的将来限制可实现的电路深度，因此在低深度下实现高性能的新方法引起了极大的兴趣。在这项工作中，我们提出了一个修改QAOA，增加了额外的变分参数的形式自由的旋转轴的$XY$平面的混合器哈密顿。通过数值模拟，我们表明，这导致了一个显着的性能改善标准QAOA在寻找解决方案的MAXCUT问题的图形高达7量子位。此外，我们探讨了我们的修改后的模拟器的Z-相位误差缓解特性，其性能下的中性原子量子处理器的现实误差模型，以及它可以在一个单一的回合解决的问题。
英文摘要：
Variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA) are particularly attractive candidates for implementation on near-term quantum processors. As hardware realities such as error and qubit connectivity will constrain achievable circuit depth in the near future, new ways to achieve high-performance at low depth are of great interest. In this work, we present a modification to QAOA that adds additional variational parameters in the form of freedom of the rotation-axis in the $XY$-plane of the mixer Hamiltonian. Via numerical simulation, we show that this leads to a drastic performance improvement over standard QAOA at finding solutions to the MAXCUT problem on graphs of up to 7 qubits. Furthermore, we explore the Z-phase error mitigation properties of our modified ansatz, its performance under a realistic error model for a neutral atom quantum processor, and the class of problems it can solve in a single round.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
