PIRSA:26050068

Optimizing fermionic Hamiltonians with classical interactions

APA

Stroeks, M. (2026). Optimizing fermionic Hamiltonians with classical interactions. Perimeter Institute for Theoretical Physics. https://pirsa.org/26050068

MLA

Stroeks, Maarten. Optimizing fermionic Hamiltonians with classical interactions. Perimeter Institute for Theoretical Physics, May. 22, 2026, https://pirsa.org/26050068

BibTex

          @misc{ scivideos_PIRSA:26050068,
            doi = {10.48660/26050068},
            url = {https://pirsa.org/26050068},
            author = {Stroeks, Maarten},
            keywords = {Quantum Information},
            language = {en},
            title = {Optimizing fermionic Hamiltonians with classical interactions},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {may},
            note = {PIRSA:26050068 see, \url{https://scivideos.org/pirsa/26050068}}
          }
          

Maarten Stroeks Delft University of Technology

Talk numberPIRSA:26050068
Source RepositoryPIRSA
Collection

Abstract

We consider the optimization problem (ground energy search) for fermionic Hamiltonians with classical interactions. This QMA-hard problem is motivated by the Coulomb electron-electron interaction being diagonal in the position basis, a fundamental fact that underpins electronic-structure Hamiltonians in quantum chemistry and condensed matter. We prove that fermionic Gaussian states achieve an approximation ratio of at least 1/3 for such Hamiltonians, independent of sparsity. This shows that classical interactions are sufficient to prevent the vanishing Gaussian approximation ratio observed in SYK-type models. We also give efficient semi-definite programming algorithms for Gaussian approximations to several families of traceless and positive-semidefinite classically interacting Hamiltonians, with the ability to enforce a fixed particle number. The technical core of our results is the concept of a Gaussian blend, a construction for Gaussian states via mixtures of covariance matrices.