activity
20052026
most citedThe Hubbard model: A computational perspective

408 citations · 446 across the 10 of their papers we have counts for

collaborators

10 papers

cond-mat.str-el2026

Fast two-dimensional tensor-network contraction via subspace iteration

Yining Zhang, Philippe Corboz

The corner transfer matrix renormalization group (CTMRG) is one of the standard contraction methods for infinite projected entangled-pair states (iPEPS), but its computational cost…

cond-mat.str-el2025

Topological and Trivial Valence-Bond Orders in Higher-Spin Kitaev Models

Xing-Yu Zhang, Qi Yang, Philippe Corboz +2

We investigate the quantum phases of higher-spin Kitaev models using tensor network methods. Our results reveal distinct bond-ordered phases for spin-1, spin-, and sp…

cond-mat.str-el2025★ 2 cited

Accelerating two-dimensional tensor network optimization by preconditioning

Xing-Yu Zhang, Qi Yang, Philippe Corboz +2

We revisit gradient-based optimization for infinite projected entangled pair states (iPEPS), a tensor network ansatz for simulating many-body quantum systems. This approach is hind…

cond-mat.str-el2025★ 2 cited

Efficient iPEPS Simulation on the Honeycomb Lattice via QR-based CTMRG

Qi Yang, Philippe Corboz

We develop a QR-based corner transfer matrix renormalization group (CTMRG) framework for contracting infinite projected entangled-pair states (iPEPS) on honeycomb lattices. Our met…

cond-mat.str-el2025★ 1 cited

Accelerating two-dimensional tensor network contractions using QR decompositions

Yining Zhang, Qi Yang, Philippe Corboz

Infinite projected entangled-pair states (iPEPS) provide a powerful tool for studying strongly correlated systems directly in the thermodynamic limit. A core component of the algor…

cond-mat.stat-mech2024

Effective dimensional reduction of complex systems based on tensor networks

Wout Merbis, Madelon Geurts, Clélia de Mulatier +1

The exact treatment of Markovian models of complex systems requires knowledge of probability distributions exponentially large in the number of components . Mean-field approxima…