使用变分虚时马约拉纳演化的硬件高效基态制备

中文摘要 AI 辅助

紧凑的基态制备电路对于早期容错量子化学至关重要。我们引入了变分虚时马约拉纳演化（VIME），这是一种经典的预训练算法，它将算子投影变分量子虚时演化（OVQITE）扩展到分子电子结构，使用单个编译的马约拉纳传播（MP）代理图。我们将VIME与我们开发的压缩平铺幺正乘积态（c-tUPS）拟设配对，以减少经典模拟和量子态制备成本。在强关联的钌配合物TLD-1411中，需要多达52个量子比特的活性空间计算达到了与DMRG参考能量相当的化学精度，其能量误差比基于ADAPT-VQE的变分马约拉纳传播（ADAPT-VMPE）小约400倍。在强多参考的并苯系列直至七并苯（60个量子比特）中，VIME匹配或改进了ADAPT-VMPE比较点的MP报告能量精度，同时使用最多减少26倍的CNOT门和降低167倍的双量子比特深度。这些结果确立了压缩变分拟设的虚时经典预训练作为资源高效分子基态制备的一条有前景的途径。

英文摘要

Compact ground-state preparation circuits are essential for early fault-tolerant quantum chemistry. We introduce Variational Imaginary-time Majorana Evolution (VIME), a classical pre-training algorithm that extends operator-projected variational quantum imaginary-time evolution (OVQITE) to molecular electronic structure using a single compiled Majorana-propagation (MP) surrogate graph. We pair VIME with a compressed tiled Unitary Product State (c-tUPS) ansatz that we develop to reduce both classical simulation and quantum state-preparation costs. In the strongly correlated ruthenium complex TLD-1411, active-space calculations requiring up to 52 qubits achieve chemical precision against DMRG reference energies, with energy errors approximately $400\times$ smaller than those from ADAPT-VQE-based variational Majorana-propagation (ADAPT-VMPE). Across the strongly multireference acene series up to heptacene (60 qubits), VIME matches or improves on the MP-reported energy accuracy of the ADAPT-VMPE comparison points with up to $26\times$ fewer CNOTs and $167\times$ lower two-qubit depth. These results establish imaginary-time classical pre-training of compressed variational ansätze as a promising route to resource-efficient molecular ground-state preparation.

发表机构

• IQM Quantum Computers GmbH（IQM量子计算机有限公司）
• RPTU Kaiserslautern-Landau（莱布尼茨技术大学凯泽斯劳滕-兰道分校）
• Universidad Autónoma de Madrid（马德里自治大学）
• Technical University of Munich（慕尼黑工业大学）

机构由 AI 辅助整理，请以论文原文为准。

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52 pages, 28 figures, 16 tables