Optimizing excited states in quantum Monte Carlo: A reassessment of double excitations

Quantum Monte Carlo (QMC) methods have proven to be highly accurate for computing excited states, but the choice of optimization strategies for multiple states remains an active topic of investigation. In this work, we revisit the calculation of double excitation energies in nitroxyl, glyoxal, tetrazine, and cyclopentadienone, exploring different objective functionals and their impact on the accuracy and robustness of QMC. A previous study for these systems employed a penalty functional to enforce orthogonality among the states, but the chosen prefactors did not strictly ensure convergence to the target states. Here, we confirm the reliability of previous results by comparing excitation energies obtained with different functionals and analyzing their consistency. In addition, we investigate the performance of different functionals when starting from a pre-collapsed excited state, providing insight into their ability to recover the target wave functions.