Cover Image

Redefining Neural Operators in $d+1$ Dimensions for Embedding Evolution

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Song, Haoze, Li, Zhihao, Zhang, Xiaobo, Gan, Zecheng, Lai, Zhilu, Wang, Wei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
by: Li, Zhihao, et al.
Published: (2024)

Structure-Aware Epistemic Uncertainty Quantification for Neural Operator PDE Surrogates
by: Song, Haoze, et al.
Published: (2026)

From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
by: Li, Zhihao, et al.
Published: (2026)

M2NO: An Efficient Multi-Resolution Operator Framework for Dynamic Multi-Scale PDE Solvers
by: Li, Zhihao, et al.
Published: (2024)

On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators
by: Liu, Wei, et al.
Published: (2025)

Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach
by: Zhang, Zecheng
Published: (2024)

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

SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networks
by: Kumar, Rahul, et al.
Published: (2025)

The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results
by: Brix, Christopher, et al.
Published: (2024)

Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade
by: Yi, Letian, et al.
Published: (2025)

Exact and Asymptotically Complete Robust Verifications of Neural Networks via Quantum Optimization
by: Li, Wenxin, et al.
Published: (2026)

Graph Neural Networks on Quantum Computers
by: Liao, Yidong, et al.
Published: (2024)

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

Formal Synthesis of Certifiably Robust Neural Lyapunov-Barrier Certificates
by: Wang, Chengxiao, et al.
Published: (2026)

Neural-Quantum-States Impurity Solver for Quantum Embedding Problems
by: Zhou, Yinzhanghao, et al.
Published: (2025)

Rethink the Role of Neural Decoders in Quantum Error Correction
by: Yan, Ge, et al.
Published: (2026)

Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
by: Ordóñez, Sebastián Andrés Cajas, et al.
Published: (2025)

UniFluids: Unified Neural Operator Learning with Conditional Flow-matching
by: Li, Haosen, et al.
Published: (2026)

Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions
by: Zheng, Wenjie, et al.
Published: (2022)

ResMGCN: Residual Message Graph Convolution Network for Fast Biomedical Interactions Discovering
by: Yin, Zecheng
Published: (2023)

Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation
by: Cheng, Jinglei, et al.
Published: (2024)

Operator Feature Neural Network for Symbolic Regression
by: Deng, Yusong, et al.
Published: (2024)

Diagonal Adaptive Non-local Observables on Quantum Neural Networks
by: Tseng, Huan-Hsin, et al.
Published: (2026)

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension
by: Qian, Hong, et al.
Published: (2026)

TG-NAS: Generalizable Zero-Cost Proxies with Operator Description Embedding and Graph Learning for Efficient Neural Architecture Search
by: Qiao, Ye, et al.
Published: (2024)

ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networks
by: Afane, Mohamed, et al.
Published: (2025)

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

LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection
by: Zhang, Haohui, et al.
Published: (2026)

Pre-training with Synthetic Data Helps Offline Reinforcement Learning
by: Wang, Zecheng, et al.
Published: (2023)

Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
by: Suzuki, Ryo, et al.
Published: (2026)

Quantum Optimization for Training Quantum Neural Networks
by: Liao, Yidong, et al.
Published: (2021)

QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization
by: Wang, Hanrui, et al.
Published: (2021)

CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
by: Song, Bowen, et al.
Published: (2025)

Single-Qudit Quantum Neural Networks for Multiclass Classification
by: Souza, Leandro C., et al.
Published: (2025)

Adaptive Non-local Observable on Quantum Neural Networks
by: Lin, Hsin-Yi, et al.
Published: (2025)

Regression and Classification with Single-Qubit Quantum Neural Networks
by: Souza, Leandro C., et al.
Published: (2024)

Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
by: Bosco, Daniele Lizzio, et al.
Published: (2024)

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees
by: Wang, Cangqing, et al.
Published: (2024)

Efficiently Computing Compact Formal Explanations
by: Wu, Min, et al.
Published: (2024)

Feature Entanglement-based Quantum Multimodal Fusion Neural Network
by: Wu, Yu, et al.
Published: (2026)