#tensor networks

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18 papers match

cs.ET2026

Optimizing Memory Efficiency and Index Ordering to Simulate Quantum Circuits Using Tensor Decision Diagrams

Vicente López Oliva, José Manuel Badía Contelles, Maria Isabel Castillo Catalán

The paper presents hardware-aware optimizations for the Fast Tensor Decision Diagram framework, improving memory management and introducing a new index-ordering heuristic to enable…

#quantum circuit simulation#tensor networks#decision diagrams#memory optimization
quant-ph2026

Benchmarking Quantum Simulations of the Lipkin-Meshkov-Glick Model Using Large Tensor Networks

Maggie Bao, Rushil Dandamudi, Jerimiah Wright +4

The paper benchmarks classical tensor‑network methods (DMRG) against NISQ quantum algorithms (VQE and SQD) for computing ground‑state energies of the Lipkin‑Meshkov‑Glick model, pr…

#quantum simulation#tensor networks#dmrg#vqe
quant-ph2026

Classical Tensor Network and Quantum Fourier Transform Approaches for Large-Scale Carr-Madan Option Pricing

Sascha Hauck, Ivica Turkalj

The paper reformulates the Carr‑Madan Fourier option‑pricing method using tensor‑network techniques, specifically a Superfast Fourier Transform (a compressed Tensor‑Train version o…

#option pricing#fourier methods#tensor networks#quantum Fourier transform
physics.chem-ph2026

Tree Tensor Networks Methods for Efficient Calculation of Molecular Vibrational Spectra

Shuo Sun, Richard M. Milbradt, Stefan Knecht +2

The paper introduces Tree Tensor Networks for calculating molecular vibrational spectra, testing various tree architectures and eigensolvers on high‑dimensional oscillator models a…

#tensor networks#vibrational spectra#molecular quantum dynamics#eigensolver algorithms
cond-mat.str-el2026

Fast two-dimensional tensor-network contraction via subspace iteration

Yining Zhang, Philippe Corboz

The paper proposes a subspace-iteration CTMRG method that replaces large SVDs with smaller ones using QR-based projectors, greatly speeding up iPEPS tensor-network contractions and…

#tensor networks#iPEPS#CTMRG#gpu acceleration
quant-ph2026

Implicit differentiation of tensor network algorithms

Lander Burgelman, Anna Francuz, Paul Brehmer +4

The paper applies implicit differentiation to the gradient computation in projected entangled-pair state (PEPS) optimization, reducing computational cost and eliminating numerical…

#tensor networks#projected entangled-pair states#implicit differentiation#gradient optimization