activity
20152025
most citedSuperfluid density and quasi-long-range order in the one-dimensional disordered Bose-Hubbard model

41 citations · 68 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.str-el2025★ 3 cited

Efficient optimization and conceptual barriers in variational finite Projected Entangled-Pair States

Daniel Alcalde Puente, Erik Lennart Weerda, Konrad Schröder +1

Projected entangled pair states (PEPS) on finite two-dimensional lattices are a natural ansatz for representing ground states of local many-body Hamiltonians, as they inherently sa…

cond-mat.str-el2025★ 6 cited

Variationally optimizing infinite projected entangled-pair states at large bond dimensions: A split corner transfer matrix renormalization group approach

Jan Naumann, Erik Lennart Weerda, Jens Eisert +2

Projected entangled-pair states (PEPS) have become a powerful tool for studying quantum many-body systems in the condensed matter and quantum materials context, particularly with a…

quant-ph2024

Learning Feedback Mechanisms for Measurement-Based Variational Quantum State Preparation

Daniel Alcalde Puente, Matteo Rizzi

This work introduces a self-learning protocol that incorporates measurement and feedback into variational quantum circuits for efficient quantum state preparation. By combining pro…

cond-mat.str-el2023★ 18 cited

An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation

Jan Naumann, Erik Lennart Weerda, Matteo Rizzi +2

Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the…

cond-mat.dis-nn2015★ 41 cited

Superfluid density and quasi-long-range order in the one-dimensional disordered Bose-Hubbard model

Matthias Gerster, Matteo Rizzi, Ferdinand Tschirsich +3

We study the equilibrium properties of the one-dimensional disordered Bose-Hubbard model by means of a gauge-adaptive tree tensor network variational method suitable for systems wi…