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arXiv 2409.15433quant-ph
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用于增强绝热量子态制备的谱隙优化

Spectral Gap Optimization for Enhanced Adiabatic State Preparation

Kshiti Sneh Rai, Jin-Fu Chen, Patrick Emonts, Jordi Tura

AI总结:

针对绝热量子态制备受路径最小谱隙限制的问题,本研究利用母哈密顿量构造自由度最大化谱隙,提出高效制备张量网络态的绝热量子算法,适用于单射与非单射张量,经多种典型量子态实例验证有效。

摘要:

非平凡量子态的制备对量子多体物理研究至关重要。这类态可通过绝热量子算法制备,而该算法受限于演化路径上的最小谱隙。本快报提出了一种高效的绝热量子方法,用于制备张量网络态(TNSs)。我们利用母哈密顿量构造中的自由度来最大化谱隙。我们通过一维随机张量网络态、AKLT态和GHZ态的实例,验证了这种制备张量网络态的高效绝热量子算法。该哈密顿量优化方法既适用于单射张量也适用于非单射张量,对后者可利用张量中存在的对称性实现。

英文摘要:

The preparation of non-trivial states is crucial to the study of quantum many-body physics. Such states can be prepared with adiabatic quantum algorithms, which are restricted by the minimum spectral gap along the path. In this letter, we propose an efficient method to adiabatically prepare tensor networks states (TNSs). We maximize the spectral gap leveraging degrees of freedom in the parent Hamiltonian construction. We demonstrate this efficient adiabatic algorithm for preparing TNS, through examples of random TNS in one dimension, AKLT, and GHZ states. The Hamiltonian optimization applies to both injective and non-injective tensors, in the latter case by exploiting symmetries present in the tensors.

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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