Bayesian phase difference estimation algorithm for direct calculation of fine structure splitting: accelerated simulation of relativistic and quantum many-body effects

IF 2.9 Q3 CHEMISTRY, PHYSICAL
Kenji Sugisaki, Srinivasa Prasannaa, Satoshi Ohshima, Takahiro Katagiri, Yuji Mochizuki, Bijaya Kumar Sahoo, Bhanu Pratap Das
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引用次数: 4

Abstract

Abstract Despite rapid progress in the development of quantum algorithms in quantum computing as well as numerical simulation methods in classical computing for atomic and molecular applications, no systematic and comprehensive electronic structure study of atomic systems that covers almost all of the elements in the periodic table using a single quantum algorithm has been reported. In this work, we address this gap by implementing the recently-proposed quantum algorithm, the Bayesian phase difference estimation (BPDE) approach, to determine fine structure splittings of a wide range of boron-like atomic systems. Since accurate estimate of fine structure splittings strongly depend on the relativistic as well as quantum many-body effects, our study can test the potential of the BPDE approach to produce results close to the experimental values. Our numerical simulations reveal that the BPDE algorithm, in the Dirac–Coulomb–Breit framework, can predict fine structure splittings of ground states of the considered systems quite precisely. We performed our simulations of relativistic and electron correlation effects on Graphics Processing Unit by utilizing NVIDIA’s cuQuantum, and observe a ×42.7 speedup as compared to the Central Processing Unit-only simulations in an 18-qubit active space.
直接计算精细结构分裂的贝叶斯相位差估计算法:相对论和量子多体效应的加速模拟
尽管量子计算中的量子算法以及原子和分子经典计算中的数值模拟方法发展迅速,但尚未有报道使用单一量子算法对几乎涵盖元素周期表中所有元素的原子系统进行系统和全面的电子结构研究。在这项工作中,我们通过实施最近提出的量子算法,贝叶斯相差估计(BPDE)方法来解决这一差距,以确定广泛的类硼原子系统的精细结构分裂。由于精细结构分裂的准确估计强烈依赖于相对论和量子多体效应,因此我们的研究可以测试BPDE方法产生接近实验值的结果的潜力。我们的数值模拟表明,在Dirac-Coulomb-Breit框架下,BPDE算法可以相当精确地预测所考虑系统基态的精细结构分裂。我们利用NVIDIA的cuQuantum在图形处理单元上进行了相对论和电子相关效应的模拟,并在18量子位的活动空间中观察到与仅中央处理单元的模拟相比×42.7加速。
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来源期刊
CiteScore
3.70
自引率
11.50%
发文量
46
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