We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can be calculated, including off-diagonal expectations, such as the transition dipole moment. The method works particularly well with neural network ansätze, and by combining this method with the FermiNet and Psiformer ansätze, we can accurately recover excitation energies and oscillator strengths on a range of molecules. We achieve accurate vertical excitation energies on benzene-scale molecules, including challenging double excitations. Beyond the examples presented in this work, we expect that this technique will be of interest for atomic, nuclear, and condensed matter physics.

中文翻译：

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利用神经网络精确计算量子激发态


我们提出了一种通过变分蒙特卡罗估计量子系统激发态的算法，该算法没有自由参数，不需要状态正交化，而是将问题转化为寻找扩展系统的基态问题。可以计算任意可观测量，包括非对角线期望，例如跃迁偶极矩。该方法特别适用于神经网络 ansätze，通过将该方法与 FermiNet 和 Psiformer ansätze 相结合，我们可以准确地恢复一系列分子的激发能和振荡器强度。我们在苯级分子上实现了精确的垂直激发能，包括具有挑战性的双激发。除了这项工作中提供的例子之外，我们预计这项技术将对原子、核和凝聚态物理学感兴趣。