Introduces the Born-Reciprocal Tensor Network to realize UV/IR mixing as an entanglement bridge in renormalization geometry, with a large-volume limit restoring standard Wilsonian decoupling.
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2010.Log-Gases and Random Matrices (LMS-34)
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Quantum coherences bind to hydrodynamic voids forming polaron-like objects, parametrically enhancing lifetimes and producing subdiffusive Green's functions in charge-conserving dynamics.
First tensor-network simulation of real-time hadronic scattering in (1+1)D SU(2) lattice gauge theory reveals entanglement and spatial delocalization in the baryon-number-one sector at strong coupling.
An adaptive patching method exploits block-sparse QTT structures to reduce computational costs for tensor contractions and enables efficient evaluation of bubble diagrams and Bethe-Salpeter equations.
Self-interactions in scalar and gauge theories suppress gravitational particle production in a quench modeling cosmic expansion, as computed with tensor networks.
A tensor-network method converts ultra-large tight-binding problems into compressible many-body problems on L pseudospins and evaluates observables without explicit matrix storage or diagonalization.
QCommute is a new C++ tool for algebraic symbolic computation of nested commutators in quantum spin-1/2 many-body systems on hypercubic lattices in the thermodynamic limit.
SeQuant introduces a graph-theoretic tensor network canonicalizer for efficient symbolic manipulation and numerical evaluation of tensors over commutative and non-commutative rings, with support for noncovariant and nested tensors.
Presents a tensor-parallel distributed MPS method with block-cyclic partitioning and pivoted QR that emulates Google's RCS benchmark at bond dimension 16384 on 32 nodes, claiming three orders of magnitude better accuracy than prior methods.
Linear Stabilizer Entropy serves as a proper non-stabilizerness monotone with overwhelming probability for non-adaptive Clifford channels on flat mixed stabilizer states, with violation probability decaying exponentially in system size.
Tensor network calculation of magic and entanglement in SU(2) lattice gauge theory ground state shows a crossover from magic-rich to less-magic regime at g_star.
Review of integrable anyonic chains with new examples identified for su(2)_k, Tambara-Yamagami TY(Z_n), Fib x Fib, Fib x Ising, and preliminary results for Haagerup-Izumi categories.
Backreaction in semiclassical scalar QED in 1+1D avoids instabilities and produces over-screening at high external charges.
The study uncovers symmetry-protected topological phases with coexisting spontaneous symmetry breaking in the triangular Majorana-Hubbard ladder model of interacting Majorana fermions.
In the Bose-Hubbard model, density correlation fronts propagate ballistically for all interaction strengths, while the correlation transport distance shows sub-ballistic growth in the chaotic phase due to distance-dependent long-time tails and enhanced front decay.
A quantics tensor train solver resolves the Gross-Pitaevskii equation across seven orders of magnitude in length scale in one dimension and on grids larger than a trillion points in two dimensions.
DMRG shows the Lieb ferrimagnetic phase on the trimer-chain extended Hubbard model at half filling remains stable up to V ≳ U/4 before phase separation into a doublon-populated phase and either metallic unsaturated FM or singlet phase.
A Julia package implementing Weingarten calculus and Wick contractions for symbolic Haar integration over compact groups and random matrix ensembles.
PEPSKit.jl is a Julia package that supplies high-level algorithms for ground-state, time-evolution and finite-temperature iPEPS simulations with symmetry support on various lattices.
Lecture notes and accompanying library teach replica tensor network methods to compute circuit-averaged observables in random quantum circuits by mapping them to classical statistical mechanics models.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
Introductory lecture notes on tensor networks with emphasis on matrix-product states, their algorithms, higher-dimensional generalizations, and applications to mixed states and open quantum systems, accompanied by Julia code.
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