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arXiv 2301.07477quant-phphysics.chem-ph
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在量子计算机上高效制备 Slater 行列式与关联态的浅层量子电路

Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer

Chong Hian Chee, Daniel Leykam, Adrian M. Mak, Dimitris G. Angelakis

AI总结:

本文借鉴量子机器学习的数据加载电路,提出以 $\mathcal{O}(d\log_2^2N)$ 双量子比特门深度制备 Slater 行列式和关联费米子态的可扩展浅层方案。

摘要:

费米子拟设态制备是许多量子算法中的关键子程序,例如用于量子化学和凝聚态物理应用的 Variational Quantum Eigensolver。迄今为止,制备 Slater 行列式和关联态所需的最浅电路深度至少随系统大小 $N$ 线性增长。受量子机器学习中开发的数据加载电路启发,我们提出一种替代范式,可提供更浅且可扩展的 $\mathcal{O}(d\log_2^2N)$ 双量子比特门深度电路,用于制备含 d 个费米子的此类态;相比第二量子化中的现有方法,它在 $N$ 上实现了次指数级降低,从而使近期量子设备能够利用更大的基组,对 $d\ll\mathcal{O}(N/\log_2^2N)$ 的费米子系统开展高精度研究。

英文摘要:

Fermionic ansatz state preparation is a critical subroutine in many quantum algorithms such as Variational Quantum Eigensolver for quantum chemistry and condensed matter applications. The shallowest circuit depth needed to prepare Slater determinants and correlated states to date scale at least linearly with respect to the system size $N$. Inspired by data-loading circuits developed for quantum machine learning, we propose an alternate paradigm that provides shallower, yet scalable ${\mathcal{O}}(d \log_2^2N)$ two-qubit gate depth circuits to prepare such states with d-fermions, offering a subexponential reduction in $N$ over existing approaches in second quantization, enabling high-accuracy studies of $d{\ll}{\mathcal{O}}{\left(N / \log_2^2 N\right)}$ fermionic systems with larger basis sets on near-term quantum devices.

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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