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Predicting excited states from ground state wavefunction by supervised quantum machine learning
Machine Learning: Science and Technology ( IF 6.3 ) Pub Date : 2020-10-31 , DOI: 10.1088/2632-2153/aba183
Hiroki Kawai 1, 2 , Yuya O. Nakagawa 3
Affiliation  

Excited states of molecules lie in the heart of photochemistry and chemical reactions. The recent development in quantum computational chemistry leads to inventions of a variety of algorithms that calculate the excited states of molecules on near-term quantum computers, but they require more computational burdens than the algorithms for calculating the ground states. In this study, we propose a scheme of supervised quantum machine learning which predicts the excited-state properties of molecules only from their ground state wavefunction resulting in reducing the computational cost for calculating the excited states. Our model is comprised of a quantum reservoir and a classical machine learning unit which processes the measurement results of single-qubit Pauli operators with the output state from the reservoir. The quantum reservoir effectively transforms the single-qubit operators into complicated multi-qubit ones which contain essential information of the system, so that the cl...

中文翻译:

通过监督量子机器学习从基态波函数预测激发态

分子的激发态位于光化学和化学反应的中心。量子计算化学的最新发展催生了各种算法的发明,这些算法可以在短期量子计算机上计算分子的激发态,但与计算基态的算法相比,它们需要更多的计算负担。在这项研究中,我们提出了一种监督量子机器学习的方案,该方案仅从分子的基态波函数预测分子的激发态特性,从而降低了计算激发态的计算成本。我们的模型由一个量子储库和一个经典的机器学习单元组成,该单元用储库的输出状态处理单量子位Pauli算子的测量结果。
更新日期:2020-11-02
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