Variational subspace methods and application to improving variational Monte Carlo dynamics
Adrien Kahn, Luca Gravina, and Filippo Vicentini
Quantum 10, 2082 (2026). https://doi.org/10.22331/q-2026-04-23-2082 We present a formalism that allows for the direct manipulation and optimization of subspaces, circumventing the need to optimize individual states when using subspace methods. Using the determinant state mapping, we can naturally extend notions such as distance and energy to subspaces, as well as Monte Carlo estimators, recovering the excited states estimation method proposed by Pfau et al. As a practical application, we then in