Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
We introduce a tensor renormalization group scheme for coarse graining a two-dimensional tensor network that can be successfully applied to both classical and quantum systems on and off criticality. The key innovation in our scheme is to deform a 2D tensor network into small loops and then optimize the tensors on each loop. In this way, we remove short-range entanglement at each iteration step and significantly improve the accuracy and stability of the renormalization flow. We demonstrate our algorithm in the classical Ising model and a frustrated 2D quantum model.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1103/PhysRevLett.118.110504 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Quantum Information Theory on Sparse Wave Functions and Applications for Quantum Chemistry.
J Phys Chem A
September 2025
Dipartimento di Scienze Fisiche e Chimiche, Università degli Studi dell'Aquila, Coppito, L'Aquila 67100, Italy.
In recent years Quantum Computing prominently entered in the field of Computational Chemistry, importing and transforming computational methods and ideas originally developed within other disciplines, such as Physics, Mathematics and Computer Science into algorithms able to estimate quantum properties of atoms and molecules on present and future quantum devices. An important role in this contamination process is attributed to Quantum Information techniques, having the 2-fold role of contributing to the analysis of electron correlation and entanglements and guiding the construction of wave function variational ansatzes for the Variational Quantum Eigensolver technique. This paper introduces the tool SparQ (Sparse Quantum state analysis), designed to efficiently compute fundamental quantum information theory observables on post-Hartree-Fock wave functions sparse in their definition space.
View Article and Find Full Text PDFTree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments.
J Chem Phys
September 2025
Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.
We introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system-bath interaction. These master equations are constructed based on the time-dependent Dirac-Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz.
View Article and Find Full Text PDFVortioxetine Improves Brain Glymphatic System Function, Functional Connectivity, and Cognitive Functions in Major Depressive Disorder.
Depress Anxiety
September 2025
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China.
The therapeutic effects of vortioxetine on mood and cognition have been documented in major depressive disorder (MDD). This study aims to examine whether vortioxetine can improve brain glymphatic system function and connections among functional brain networks and to explore the underlying relationships among these changes. A total of 34 patients with MDD and 41 healthy controls (HCs) were recruited in the study.
View Article and Find Full Text PDFExploring the interaction of reading and attention through connectivity with the frontal-eye-field.
Neuroscience
September 2025
Department of Psychology & Health Studies, University of Saskatchewan, Saskatoon, Canada. Electronic address:
Attentional processes are crucial to ensure successful reading, and theories of dyslexia propose that dysfunctional attention networks may contribute to the observed reading deficits. The goals of this study were to localize a region of the frontal-eye-field (FEF) involved in both reading and attention and examine its connectivity with regions in the reading and attention networks, given the known role of the FEF in attentional processes and theorized role in reading. In Experiment 1, we revisited the results of our previous hybrid reading and attention study.
View Article and Find Full Text PDFLoRA-PT: Low-rank adapting UNETR for hippocampus segmentation using principal tensor singular values and vectors.
Artif Intell Med
September 2025
Department of Mathematics, Hangzhou Dianzi University, Hangzhou 310018, China. Electronic address:
The hippocampus is an important brain structure involved in various psychiatric disorders, and its automatic and accurate segmentation is vital for studying these diseases. Recently, deep learning-based methods have made significant progress in hippocampus segmentation. However, training deep neural network models requires substantial computational resources, time, and a large amount of labeled training data, which is frequently scarce in medical image segmentation.
View Article and Find Full Text PDF