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arXiv 2212.03005quant-phphysics.chem-ph
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基于量子-经典混合神经网络的基态和激发态势能面推断

Potential energy surfaces inference of both ground and excited state using hybrid quantum-classical neural network

Yasutaka Nishida, Fumihiko Aiga

摘要:

随着量子计算研究的不断深入,变分量子本征求解器(VQE)作为近期量子计算机的一种可能的应用而受到广泛关注。虽然VQE经常被应用于量子化学,但可靠的结果需要高的计算成本,因为需要无限多的测量来获得准确的期望值,并且期望值被计算多次以最小化变分优化过程中的成本函数。因此,有必要降低计算成本的VQE的实际任务,如估计的势能面(PES)的化学精度,这是特别重要的分子结构和化学反应动力学的分析。最近提出了一种混合量子经典神经网络用于VQE的替代建模[Xia $et\ al$,Entropy 22,828(2020)]。利用该模型,可以准确地推导出简单分子(如H2)的基态能量。在这项研究中,我们已经扩展了该模型,通过使用子空间搜索变分量子本征求解器程序,使PES的基态和激发态可以推断与化学的准确性。我们还通过使用IBM的QASM后端演示了采样噪声对预训练模型性能的影响。

英文摘要:

Reflecting the increasing interest in quantum computing, the variational quantum eigensolver (VQE) has attracted much attentions as a possible application of near-term quantum computers. Although the VQE has often been applied to quantum chemistry, high computational cost is required for reliable results because infinitely many measurements are needed to obtain an accurate expectation value and the expectation value is calculated many times to minimize a cost function in the variational optimization procedure. Therefore, it is necessary to reduce the computational cost of the VQE for a practical task such as estimating the potential energy surfaces (PESs) with chemical accuracy, which is of particular importance for the analysis of molecular structures and chemical reaction dynamics. A hybrid quantum-classical neural network has recently been proposed for surrogate modeling of the VQE [Xia $et\ al$, Entropy 22, 828 (2020)]. Using the model, the ground state energies of a simple molecule such as H2 can be inferred accurately without the variational optimization procedure. In this study, we have extended the model by using the subspace-search variational quantum eigensolver procedure so that the PESs of the both ground and excited state can be inferred with chemical accuracy. We also demonstrate the effects of sampling noise on performance of the pre-trained model by using IBM's QASM backend.

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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