Year: 2026
Type: article
Abstract: 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 introduce Bridge, a method that improves the performance of variational dynamics by extracting linear combinations of variational time-evolved states. We find that Bridge is both computationally inexpensive and capable of significantly mitigating the errors that arise from discretizing the dynamics, and can thus be systematically used as a post-processing tool for variational dynamics.
Source: Quantum
Authors: Adrien Kahn, Luca Gravina, Filippo Vicentini
Institutions: Centre National de la Recherche Scientifique, École Polytechnique, Collège de France, Université Paris Sciences et Lettres, Centre de Physique Théorique, Centre Inria de Saclay, Laboratoire d'Informatique de l'École Polytechnique, Institut Polytechnique de Paris, École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $215
Awards: G8475297429, G8969046659
OpenAlex ID: W4414688641