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arXiv 2311.02151quant-ph
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应用于自旋玻璃模型的奇偶编码量子近似优化算法的性能消失

Vanishing performance of the parity-encoded quantum approximate optimization algorithm applied to spin-glass models

Elisabeth Wybo, Martin Leib

AI总结:

该研究在自旋玻璃模型上基准测试奇偶编码 QAOA,证明固定层数下其性能随问题规模按 $N^{-1/2}$ 衰减至随机猜测水平,并用张量网络验证其扩展性弱于标准 QAOA。

摘要:

奇偶映射为量子近似优化算法(QAOA)提供了一种几何局部的编码,但代价是对于全连接问题会产生二次量级的量子比特开销。在本工作中,我们在自旋玻璃模型上对奇偶编码的 QAOA 进行基准测试。我们探讨了该算法扩展方面的开放性问题。特别地,我们表明,对于固定层数的奇偶编码 QAOA,其性能或输出能量会随问题规模 $N$ 以 $N^{-1/2}$ 的形式消失并趋于零(即随机猜测所能达到的值)。我们的结果表明,与标准版本的 QAOA 相比,奇偶编码的 QAOA 并不具备有前景的扩展性。我们通过张量网络计算证实了这些结果,并对最优 QAOA 参数在不同问题实例上的集中现象进行了评述。

英文摘要:

The parity mapping provides a geometrically local encoding of the Quantum Approximate Optimization Algorithm (QAOA), at the expense of having a quadratic qubit overhead for all-to-all connected problems. In this work, we benchmark the parity-encoded QAOA on spin-glass models. We address open questions in the scaling of this algorithm. In particular, we show that for fixed number of parity-encoded QAOA layers, the performance or the output energy, vanishes towards zero (the value achieved by random guessing) with problem size $N$ as $N^{-1/2}$. Our results suggest that the parity-encoded QAOA does not have a promising scaling compared to the standard version of QAOA. We perform tensor-network calculations to confirm our results, and comment on the concentration of optimal QAOA parameters over problem instances.

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。

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