Understanding the properties of excited states of complex molecules is crucial for many chemical and physical processes. Calculating these properties is often significantly more resource-intensive than calculating their ground state counterparts. We present a quantum machine learning model that predicts excited-state properties from the molecular ground state for different geometric configurations. The model comprises a symmetry-invariant quantum neural network and a conventional neural network and is able to provide accurate predictions with only a few training data points. The proposed procedure is fully NISQ compatible. This is achieved by using a quantum circuit that requires a number of parameters linearly proportional to the number of molecular orbitals, along with a parameterized measurement observable, thereby reducing the number of necessary measurements. We benchmark the algorithm on three different molecules by evaluating its performance in predicting excited state transition energies and transition dipole moments. We show that, in many instances, the procedure is able to outperform various classical models that rely solely on classical features.

翻译：理解复杂分子的激发态性质对于许多化学和物理过程至关重要。计算这些性质通常比计算其基态性质需要显著更多的资源。我们提出了一种量子机器学习模型，该模型能够针对不同几何构型，从分子基态预测激发态性质。该模型包含一个对称不变的量子神经网络和一个传统神经网络，仅需少量训练数据点即可提供精确预测。所提出的流程完全兼容含噪声中等规模量子（NISQ）设备。这是通过使用一个所需参数数量与分子轨道数呈线性比例的量子电路，以及一个参数化的测量可观测量来实现的，从而减少了必要的测量次数。我们通过在三个不同分子上评估其在预测激发态跃迁能和跃迁偶极矩方面的性能，对该算法进行了基准测试。结果表明，在许多情况下，该流程能够优于仅依赖经典特征的各种经典模型。