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arXiv 2403.01043quant-phcond-mat.str-el
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利用量子增强密度矩阵下折叠构建有效哈密顿量的要求

Requirements for building effective Hamiltonians using quantum-enhanced density matrix downfolding

Shivesh Pathak, Antonio E. Russo, Stefan Seritan, Alicia B. Magann, Eric Bobrow, Andrew J. Landahl, Andrew D. Baczewski

AI总结:

本文提出混合量子-经典DMD协议以消除经典低能子空间采样的系统误差,明确三项可行性要求并评估两类强关联模型的实现资源。

摘要:

密度矩阵下折叠(DMD)是一种从量子多体哈密顿量回归低能有效哈密顿量的技术。经典DMD实现精度的一个限制因素,是在近似低能子空间上采样量子多体系统可观测量时伴随的难以量化的系统误差。我们提出一种混合量子-经典协议来规避这一限制,它依赖量子计算机在定义明确的低能子空间中高效制备并采样状态、且精度可系统性提升的潜在能力。我们介绍了实现这一点的三项要求,其中包括一个可压缩性概念,用于量化量子DMD可能高效适用的哈密顿量及其低能子空间的特征。在这些要求得到满足的假设下,我们分析了协议的设计选择,并给出了在掺杂二维Fermi-Hubbard模型和铜氧化物超导体的ab initio模型上实现量子增强DMD的资源估计。

英文摘要:

Density matrix downfolding (DMD) is a technique for regressing low-energy effective Hamiltonians from quantum many-body Hamiltonians. One limiting factor in the accuracy of classical implementations of DMD is the presence of difficult-to-quantify systematic errors attendant to sampling the observables of quantum many-body systems on an approximate low-energy subspace. We propose a hybrid quantum-classical protocol for circumventing this limitation, relying on the prospective ability of quantum computers to efficiently prepare and sample from states in well-defined low-energy subspaces with systematically improvable accuracy. We introduce three requirements for when this is possible, including a notion of compressibility that quantifies features of Hamiltonians and low-energy subspaces thereof for which quantum DMD might be efficient. Assuming that these requirements are met, we analyze design choices for our protocol and provide resource estimates for implementing quantum-enhanced DMD on both the doped 2-D Fermi-Hubbard model and an ab initio model of a cuprate superconductor.

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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