Matrix Product States for Modulated Symmetries: SPT, LSM, and Beyond

MPS formalism generalized to systems with modulated symmetries via new push-through conditions, enabling 1D SPT phase classification and Lieb–Schultz–Mattis constraints for exponential, dipole, and non-Abelian symmetry groups.

#QuantumPhases #TensorNetworks #Research

Matrix Product Quantum Channels: Structure, Classification, and Implementation

New framework for Matrix Product Quantum Channels (MPQCs) proves all locally purified 1D channels form a single equivalence class, and that long-range entangled channels can be implemented in constant depth using two rounds of measurements and feedforward.

#QuantumChannels #TensorNetworks #Research

Certified Quantum Schrödinger Feedback Control via Hierarchical Tucker Tensor Models

Stability-certified framework for high-dimensional quantum feedback control uses Hierarchical Tucker tensor surrogates, proving practical exponential stability with rank requirements scaling only logarithmically with tracking precision.

#QuantumControl #TensorNetworks #Research

Coefficient-Decoupled MPOs as an Interface to LCU Circuits

A new MPO framework separates symbolic operator structure from a tunable coefficient bridge, enabling reusable LCU circuit compilation where only the Prep oracle updates on coefficient changes, demonstrated on electronic-structure Hamiltonians.

#QuantumAlgorithms #TensorNetworks #Research

Tensor Network Methods for Electron-Hole Complexes in Semiconductor Nanoplatelets Beyond Confinement Limits

QTT/DMRG tensor networks solve exciton & trion Schrödinger equations in CdSe nanoplatelets beyond weak/strong confinement regimes, achieving 2048-pt/dimension resolution in megabytes vs. an infeasible 128 TiB required by direct methods.

#TensorNetworks #QuantumMaterials #Nanoplatelets

Classical Tensor Network Simulations Challenge Quantum Advantage Claims

Multiverse Computing researchers show tensor network methods (MPS/PEPS) now classically simulate up to 433 qubits, scrutinising quantum advantage claims from IBM, D-Wave & Google and raising the verification bar for genuine quantum superiority.

#QuantumAdvantage #TensorNetworks #News

Rank-Adaptive Tensor Decompositions for Dynamical Schrödinger Equation Simulation

LLNL researchers demonstrate tensor-train (MPS) methods—TDVP-2 and MPS-BUG—outperform conventional matrix-vector simulation beyond ~13 qubits for time-dependent quantum control problems, achieving linear rather than exponential scaling on a laptop.

#QuantumSimulation #TensorNetworks #Research

Successive Randomized Compression: Fast Randomized Algorithm for MPO-MPS Product

Caltech researchers introduce SRC, a single-pass randomized algorithm for compressed MPO-MPS multiplication in tensor networks. Benchmarks show up to 181× speedup over direct methods in long-range XY spin chain simulation with 101 spins.

#TensorNetworks #QuantumSimulation #News

Watching your hard-won 'quantum advantage' get demolished by a postdoc with a tensor network library and a single H100.

#QuantumComputing #TensorNetworks #QubitsOK

Variational #quantumalgorithms and classical variational methods, such as #tensornetworks, provide upper bounds on ground-state energies. This work provides guarantees for lower bounds.

journals.aps.org/pra/abstract...

Linear models can only scale.
Tensor models begin to shape structure.
Geometric models finally unlock emergence.
This is the real path:
matrix → tensor → manifold.
#AI #Emergence #TensorNetworks #AITheory

You can scale compute forever,
but linear models stay linear.
Tensor networks introduce structure.
Geometric models create emergence.
The transition is not about speed —
it’s about dimensionality.
#AI #GPU #TensorNetworks #Emergence

Scaling stretches a line.
Tensors open a space.
Geometry creates a world.
Emergence isn’t an accident —
it’s a structural phase shift.
#AI #Emergence #TensorNetworks

Scaling extends a line.
Tensors open a space.
Geometry creates a phase.

AI doesn’t emerge by size—
it emerges when structure changes.

matrix → tensor → manifold.
#AI #Emergence #TensorNetworks

📝🕸️ Congratulations to José Garre Rubio and András Molnár of the Schuch Group on their paper "On two-dimensional tensor network group symmetries".

🔗 Click the link for more information: schuch.univie.ac.at/news/detailv...

#NewJournalOfPhysics #ScientificPaper #QuantumInformation #TensorNetworks

New #PASQuanS2 publication is out!

The team introduces split-CTMRG, a more efficient way to contract infinite PEPS.

If you’re working with 2D quantum many-body systems, this one is worth a look.

🔗 link.aps.org/doi/10.1103/...

#quantumsimulation #tensornetworks #iPEPS #quantumphysics

AI Tensor Network Framework Enables Fast and Accurate Computation of Configurational Integrals in Materials Science Researchers at the University of New Mexico and Los Alamos National Laboratory have developed an AI-driven computational framework named THOR that efficiently solves the longstanding challenge of…

AI Tensor Network Framework Enables Fast and Accurate Computation of Configurational Integrals in Materials Science killbait.com/en/ai-tensor... #science #tensornetworks #statisticalphysics #materialsscience

Random #tensornetworks provide a powerful framework for probing and understanding complex quantum systems, especially in regimes where conventional tools fail. Here, we rigorously investigate dynamical properties of holographic toy models.

scirate.com/arxiv/2508.1...

7/
PlanqTN is here.
Build quantum codes like LEGO.
Play, hack, and contribute.
🎥 youtu.be/TNnE3hReYVk
🌐 planqtn.com
🔗 github.com/planqtn/plan...
#QuantumComputing #QEC #OpenSource #QuantumLEGO #AItools #TensorNetworks #PlanqTN

CSQM 2025 Understanding entangled quantum systems with many interacting particles is a major challenge in physics, crucial for discovering new phases of matter, designing advanced materials, and driving quantum...

I am thrilled to announce the "Challenges in Simulating Quantum Matter" workshop at ETH Zürich, 16-18 June 2025 🎉 Now accepting applications for talks and posters: csqm.org
@ethzurich.bsky.social
#NeuralQuantumStates #QuantumComputing #TensorNetworks #quantum

FREE TO READ - EDITOR'S CHOICE: Tensor networks for hierarchical lattices

by S. S. Akimenko and A. V. Myshlyavtsev

#FreeToRead #EditorsChoice #TensorNetworks

👉 iopscience.iop.org/article/10.1...

‪
Check out this #Julia package MPSDynamics.jl to simulate open quantum systems with tensor networks

#TensorNetworks #QuantumSoftware

Simulating matrix models with tensor networks Matrix models, as quantum mechanical systems without explicit spatial dependence, provide valuable insights into higher-dimensional gauge and gravitational theories, especially within the framework of...

Do you ever wonder how tensor networks could help you understand quantum gravity?
We have done the work for you and published a comprehensive guide to the performance of TN for simulating matrix models scirate.com/arxiv/2412.0...

#quantumcomputing
#quantumgtavity
#tensornetworks

Johannes Hauschild

I might have missed here, but Joannes Hauschild https://johannes-hauschild.de/ has released a new version of TENPY https://github.com/tenpy/tenpy/releases thanks Joannes #tenpy #TensorNetworks #OpenSource #OpenScience