Grassmann tensor-network method for strong-coupling QCD
Jacques Bloch, Robert Lohmayer

TL;DR
This paper introduces a tensor-network approach for simulating strong-coupling QCD with staggered quarks at nonzero chemical potential, enabling efficient computation of observables and phase transitions in low-dimensional models.
Contribution
The paper develops a Grassmann higher-order tensor renormalization group method tailored for strong-coupling QCD, including validation and preliminary results in three dimensions.
Findings
Validated method against exact results on small lattices
Observed no spontaneous chiral symmetry breaking in 2D
Indications of a first-order phase transition at finite chemical potential
Abstract
We present a tensor-network method for strong-coupling QCD with staggered quarks at nonzero chemical potential. After integrating out the gauge fields at infinite coupling, the partition function can be written as a full contraction of a tensor network consisting of coupled local numeric and Grassmann tensors. To evaluate the partition function and to compute observables, we develop a Grassmann higher-order tensor renormalization group method, specifically tailored for this model. We apply the method to the two-dimensional case and validate it by comparing results for the partition function, the chiral condensate and the baryon density with exact analytical expressions on small lattices up to volumes of . For larger two-dimensional volumes, we present tensor results for the chiral condensate as a function of the mass and volume, and observe that the chiral symmetry is not…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Chromodynamics and Particle Interactions · Black Holes and Theoretical Physics · High-Energy Particle Collisions Research
