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Q: Fachverband Quantenoptik und Photonik

Q 21: Quantum Computing and Simulation III

Q 21.4: Talk

Tuesday, March 3, 2026, 12:00–12:15, P 10

Sample-Based Krylov Quantum Diagonalization for the Schwinger Model on Trapped-Ion and Superconducting Quantum Processors — •Jurek Eisinger¹, Emil Rosanowski², Lena Funcke², Ulrich Poschinger¹, and Ferdinand Schmidt-Kaler¹ — ¹QUANTUM, University of Mainz, Department of Physics, Staudingerweg 7, Germany — ²University of Bonn, Nussallee 14-16, 53115 Bonn, Germany

We apply the Sample-based Krylov Quantum Diagonalization (SKQD) method to lattice gauge theories, using the Schwinger model with a θ-term as a benchmark. SKQD approximates the ground state of a Hamiltonian, employing a hybrid quantum*classical approach: (i) constructing a Krylov space from bitstrings sampled from time-evolved quantum states, and (ii) classically diagonalizing the Hamiltonian within this subspace. We implement the algorithm on both, trapped-ion and superconducting quantum processors, and study the dependence of the ground-state energy and particle number on the value of the θ-term, accurately capturing the model*s phase structure. A striking advantage of SKQD is the substantial reduction of the effective Hilbert space, although the Krylov space dimension still scales exponentially with the system size. Thus, SKQD is a promising method for simulating lattice gauge theories in larger volumes. The methods and results are described in more detail in [Rosanowski et al., arXiv:2510.26951 (2025)].

Keywords: Trapped-Ion Quantum Computing; Schwinger Model; Krylov Quantum Diagonalization; Lattice Gauge Theory