计算化学中噪声量子电路的数值模拟

Jerimiah Wright, Meenambika Gowrishankar, Daniel Claudino, Phillip C. Lotshaw, Thien Nguyen, Alexander J. McCaskey, Travis S. Humble
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引用次数: 4

摘要

近期量子计算机提供的计算小分子基态性质的机会取决于计算ansatz的结构以及由设备噪声引起的误差。本文研究了这些噪声量子电路的行为,利用数值模拟来估计制备的量子态相对于传统方法获得的基态的精度和保真度。为了利用变分量子特征解算器算法估计氢化钠的基态能量,我们实现了几种不同类型的由单一耦合簇理论推导的ansatz电路。我们展示了能量和保真度的相对误差如何与基于门的噪声水平、核间配置、ansatz电路深度和参数优化方法相对应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical simulations of noisy quantum circuits for computational chemistry

The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.

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期刊介绍: Journal of Materials Science: Materials Theory publishes all areas of theoretical materials science and related computational methods. The scope covers mechanical, physical and chemical problems in metals and alloys, ceramics, polymers, functional and biological materials at all scales and addresses the structure, synthesis and properties of materials. Proposing novel theoretical concepts, models, and/or mathematical and computational formalisms to advance state-of-the-art technology is critical for submission to the Journal of Materials Science: Materials Theory. The journal highly encourages contributions focusing on data-driven research, materials informatics, and the integration of theory and data analysis as new ways to predict, design, and conceptualize materials behavior.
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