Cover Image

Artificial Entanglement in the Fine-Tuning of Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Min, Wang, Zihan, Chen, Canyu, Wu, Zeguan, Li, Manling, Liu, Junyu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence High Energy Physics - Theory Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Infeasibility Aware Large Language Models for Combinatorial Optimization
by: Wang, Yakun, et al.
Published: (2026)

Estimating truncation effects of quantum bosonic systems using sampling algorithms
by: Hanada, Masanori, et al.
Published: (2022)

Artificial Neural Network in Cosmic Landscape
by: Liu, Junyu
Published: (2017)

Fine-Tuning Small Reasoning Models for Quantum Field Theory
by: Woodward, Nathaniel S., et al.
Published: (2026)

Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
by: Kundu, Akash
Published: (2025)

GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching
by: Wang, Haoran, et al.
Published: (2024)

Uncovering Singularities in Feynman Integrals via Machine Learning
by: Liu, Yuanche, et al.
Published: (2025)

Symbolic Regression for Beyond the Standard Model Physics
by: AbdusSalam, Shehu, et al.
Published: (2024)

Beyond MD17: the reactive xxMD dataset
by: Pengmei, Zihan, et al.
Published: (2023)

Les Houches Lectures on Deep Learning at Large & Infinite Width
by: Bahri, Yasaman, et al.
Published: (2023)

Physics-informed neural network solves minimal surfaces in curved spacetime
by: Hashimoto, Koji, et al.
Published: (2025)

An Analytic Theory of Quantum Imaginary Time Evolution
by: Chen, Min, et al.
Published: (2025)

Neural network representation of quantum systems
by: Hashimoto, Koji, et al.
Published: (2024)

Guided Graph Compression for Quantum Graph Neural Networks
by: Casals, Mikel, et al.
Published: (2025)

Generative Invertible Quantum Neural Networks
by: Rousselot, Armand, et al.
Published: (2023)

Uncertainty Quantification From Scaling Laws in Deep Neural Networks
by: Elsharkawy, Ibrahim, et al.
Published: (2025)

Versor: A Geometric Sequence Architecture
by: Huy, Truong Minh, et al.
Published: (2026)

Comparative Study of Neural Network Methods for Solving Topological Solitons
by: Hashimoto, Koji, et al.
Published: (2024)

AdS-GNN -- a Conformally Equivariant Graph Neural Network
by: Zhdanov, Maksim, et al.
Published: (2025)

Approximate Gaussianity Beyond Initialisation in Neural Networks
by: Hirst, Edward, et al.
Published: (2025)

Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery
by: Cao, Qing-Hong, et al.
Published: (2025)

Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
by: Hashimoto, Koji, et al.
Published: (2024)

Symbolic Analysis of Grover Search Algorithm via Chain-of-Thought Reasoning and Quantum-Native Tokenization
by: Chen, Min, et al.
Published: (2025)

Entanglement suppression and low-energy scattering of heavy mesons
by: Hu, Tao-Ran, et al.
Published: (2024)

Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
by: Lu, Ian, et al.
Published: (2024)

FeynTune: Large Language Models for High-Energy Theory
by: Richmond, Paul, et al.
Published: (2025)

Entanglement degradation of static black holes in effective quantum gravity
by: Liu, Xiaobao, et al.
Published: (2025)

Fine-Tuning Vision-Language Models for Neutrino Event Analysis in High-Energy Physics Experiments
by: Sagar, Dikshant, et al.
Published: (2025)

Entanglement Maximization and Mirror Symmetry in Two-Higgs-Doublet Models
by: Carena, Marcela, et al.
Published: (2025)

Understanding Diffusion Models by Feynman's Path Integral
by: Hirono, Yuji, et al.
Published: (2024)

Entanglement suppression and emergent symmetries in hadron scatterings
by: Hu, Tao-Ran, et al.
Published: (2025)

Quantum-classical simulation of quantum field theory by quantum circuit learning
by: Ikeda, Kazuki
Published: (2023)

Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
by: Hamid, Eyad I. B.
Published: (2026)

Exploring the holographic entropy cone via reinforcement learning
by: He, Temple, et al.
Published: (2026)

Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
by: Li, Zimu, et al.
Published: (2022)

Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)

Quantum-Inspired Fine-Tuning for Few-Shot AIGC Detection via Phase-Structured Reparameterization
by: Xing, Kaiyang, et al.
Published: (2026)

Test-time Scaling Techniques in Theoretical Physics -- A Comparison of Methods on the TPBench Dataset
by: Gao, Zhiqi, et al.
Published: (2025)

Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics
by: Chung, Daniel J. H., et al.
Published: (2025)

Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Data
by: Sanz, Veronica
Published: (2026)