浅层小量子电路的量子自然梯度与大地校正

Mourad Halla
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引用次数: 0

摘要

量子自然梯度(QNG)方法通过富比研究度量结合量子态空间的几何见解,增强了优化不变量子算法(VQAs)。在这项工作中,我们利用黎曼欧拉更新规则和大地方程引入了高阶积分器和大地校正,从而扩展了量子自然梯度(QNG)。我们还开发了计算这些修正所需的 Christoffel 符号的高效方法,利用参数偏移规则实现量子电路的直接测量。通过理论分析和实际例子,我们证明 QNGGC 比标准 QNG 显著提高了收敛率,突出了将大地校正集成到量子优化过程中的好处。我们的方法利用几何方法的优势,为更高效的量子算法铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum Natural Gradient with Geodesic Corrections for Small Shallow Quantum Circuits
The Quantum Natural Gradient (QNG) method enhances optimization in variational quantum algorithms (VQAs) by incorporating geometric insights from the quantum state space through the Fubini-Study metric. In this work, we extend QNG by introducing higher-order integrators and geodesic corrections using the Riemannian Euler update rule and geodesic equations, deriving an updated rule for the Quantum Natural Gradient with Geodesic Correction (QNGGC). QNGGC is specifically designed for small, shallow quantum circuits. We also develop an efficient method for computing the Christoffel symbols necessary for these corrections, leveraging the parameter-shift rule to enable direct measurement from quantum circuits. Through theoretical analysis and practical examples, we demonstrate that QNGGC significantly improves convergence rates over standard QNG, highlighting the benefits of integrating geodesic corrections into quantum optimization processes. Our approach paves the way for more efficient quantum algorithms, leveraging the advantages of geometric methods.
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