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...

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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。