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TT: Fachverband Tiefe Temperaturen
TT 23: Correlated Electrons – Poster I
TT 23.19: Poster
Montag, 9. März 2026, 18:00–20:00, P1
Sample-Based Quantum Diagonalization of Similarity-Transformed Hamiltonians for Strongly Correlated Systems — •Emanuele Ricci1,2 and Werner Dobrautz1,2,3,4 — 1Technical University Dresden, 01069 Dresden, Germany — 2Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, 01069 Dresden, Germany — 3Center for Advanced Systems Understanding, 02826 Görlitz, Germany — 4Helmholtz-Zentrum Dresden-Rossendorf, 01328 Dresden, Germany
One of the central challenges in electronic structure theory is the accurate description of strongly correlated systems. We address this using a hybrid quantum-classical scheme, sample-based quantum diagonalization, in which a quantum computer acts as a dedicated sampler to identify a compact, problem-adapted subspace of important Slater determinants. The corresponding reduced Hamiltonian is then diagonalized classically to obtain the ground state. To mitigate quantum errors, we apply a classical post-processing step that projects samples onto the correct particle-number sector.
Before sampling, we perform a similarity transformation of the Hamiltonian that concentrates the ground-state weight into a smaller set of determinants, reducing the relevant subspace and improving accuracy at the cost of a non-Hermitian effective Hamiltonian. While non-Hermiticity would ordinarily hinder quantum algorithms, we demonstrate that it can still be exploited within a sampling-based framework using the UCJ (unitary cluster Jastrow) ansatz initialized with single and double amplitudes from the transformed Hamiltonian.
Keywords: quantum computing; correlated system; Transcorrelated; quantum algorithm; ground-state