近日，美国哈佛大学的Susanne F. Yelin&Nishad Maskara及其研究团队取得一项新进展。经过不懈努力，他们利用可重构量子处理器实现分子和材料的可编程模拟。相关研究成果已于2025年1月22日在国际知名学术期刊《自然—物理学》上发表。

该研究团队引入一种针对由模型自旋哈密顿量表示的强关联量子系统的模拟框架，该框架利用可重构量子比特架构以可编程方式模拟实时动力学。该研究方法还引入了一种算法，通过量子测量结果的经典协同处理来提取化学相关的光谱特性。研究人员开发了一个数字-模拟混合仿真工具箱，利用数字弗洛凯工程和硬件优化的多量子比特操作，实现哈密顿量的高效时间演化，从而准确模拟复杂的自旋-自旋相互作用。作为示例，研究人员提出了一种基于里德堡原子阵列的实现方案。此外，他们展示了如何通过瞬时测量和单辅助量子比特控制从动力学中提取详细的光谱信息，从而能够从单一数据集中评估激发能和有限温度下的磁化率。为了说明该方法，他们展示了如何利用其计算多核过渡金属催化剂和二维磁性材料的关键性质。

据悉，量子化学和量子材料的模拟被认为是量子信息处理器最重要的应用之一。然而，由于将典型问题编程到量子硬件上的计算成本过高，实现这类问题的实际量子优势面临挑战。

附：英文原文

Title: Programmable simulations of molecules and materials with reconfigurable quantum processors

Author: Maskara, Nishad, Ostermann, Stefan, Shee, James, Kalinowski, Marcin, McClain Gomez, Abigail, Araiza Bravo, Rodrigo, Wang, Derek S., Krylov, Anna I., Yao, Norman Y., Head-Gordon, Martin, Lukin, Mikhail D., Yelin, Susanne F.

Issue&Volume: 2025-01-22

Abstract: Simulations of quantum chemistry and quantum materials are believed to be among the most important applications of quantum information processors. However, realizing practical quantum advantage for such problems is challenging because of the prohibitive computational cost of programming typical problems into quantum hardware. Here we introduce a simulation framework for strongly correlated quantum systems represented by model spin Hamiltonians that uses reconfigurable qubit architectures to simulate real-time dynamics in a programmable way. Our approach also introduces an algorithm for extracting chemically relevant spectral properties via classical co-processing of quantum measurement results. We develop a digital–analogue simulation toolbox for efficient Hamiltonian time evolution using digital Floquet engineering and hardware-optimized multi-qubit operations to accurately realize complex spin–spin interactions. As an example, we propose an implementation based on Rydberg atom arrays. In addition, we show how detailed spectral information can be extracted from the dynamics through snapshot measurements and single-ancilla control, enabling the evaluation of excitation energies and finite-temperature susceptibilities from a single dataset. To illustrate the approach, we show how to use the method to compute key properties of a polynuclear transition-metal catalyst and two-dimensional magnetic materials.

DOI: 10.1038/s41567-024-02738-z

Source: https://www.nature.com/articles/s41567-024-02738-z