Advancing Machine Learning Applications in Quantum Few-Body Systems
Fuente:
arXiv
Guardado en:
| Autores principales: | Ziqi, Jin, Recchia, Paolo, Gattobigio, Mario |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
The Steepest Slope toward a Quantum Few-body Solution: Gradient Variational Methods for the Quantum Few-body Problem
por: Recchia, Paolo, et al.
Publicado: (2024)
por: Recchia, Paolo, et al.
Publicado: (2024)
Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
por: Chen, Ying, et al.
Publicado: (2025)
por: Chen, Ying, et al.
Publicado: (2025)
SAFE Quantum Machine Learning with Variational Quantum Classifiers
por: Chen, Ying, et al.
Publicado: (2026)
por: Chen, Ying, et al.
Publicado: (2026)
Variational Quantum Algorithms for Many-Body Systems
por: Consiglio, Mirko
Publicado: (2025)
por: Consiglio, Mirko
Publicado: (2025)
Variational Embeddings for Many Body Quantum Systems
por: Barison, Stefano, et al.
Publicado: (2023)
por: Barison, Stefano, et al.
Publicado: (2023)
Universal Neural Propagator: Learning Time Evolution in Many-Body Quantum Systems
por: Qi, Zihao, et al.
Publicado: (2026)
por: Qi, Zihao, et al.
Publicado: (2026)
Quantum Algorithms and Applications for Open Quantum Systems
por: Delgado-Granados, Luis H., et al.
Publicado: (2024)
por: Delgado-Granados, Luis H., et al.
Publicado: (2024)
Finding the Dynamics of an Integrable Quantum Many-Body System via Machine Learning
por: Wei, Victor, et al.
Publicado: (2023)
por: Wei, Victor, et al.
Publicado: (2023)
FewBodyToolkit.jl: a Julia package for solving quantum few-body problems
por: Happ, Lucas
Publicado: (2025)
por: Happ, Lucas
Publicado: (2025)
Quantum Many-Body Scars in Few-Body Dipole-Dipole Interactions
por: Spielman, Sarah E., et al.
Publicado: (2022)
por: Spielman, Sarah E., et al.
Publicado: (2022)
XDiag: Exact Diagonalization for Quantum Many-Body Systems
por: Wietek, Alexander, et al.
Publicado: (2025)
por: Wietek, Alexander, et al.
Publicado: (2025)
Advanced simulations with PLUMED: OPES and Machine Learning Collective Variables
por: Trizio, Enrico, et al.
Publicado: (2024)
por: Trizio, Enrico, et al.
Publicado: (2024)
Machine Learning Interatomic Potentials with Keras API
por: Rili, James Paolo
Publicado: (2024)
por: Rili, James Paolo
Publicado: (2024)
Fermionic Magic Resources of Quantum Many-Body Systems
por: Sierant, Piotr, et al.
Publicado: (2025)
por: Sierant, Piotr, et al.
Publicado: (2025)
Physics-Informed Neural Networks for Maximizing Quantum Fisher Information in Time-Dependent Many-Body Systems
por: Ferrer-Sánchez, Antonio, et al.
Publicado: (2026)
por: Ferrer-Sánchez, Antonio, et al.
Publicado: (2026)
Combining Bayesian Optimization, SVD and Machine Learning for Advanced Optical Design
por: Mahani, M. R., et al.
Publicado: (2024)
por: Mahani, M. R., et al.
Publicado: (2024)
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
por: Zhu, Kai, et al.
Publicado: (2025)
por: Zhu, Kai, et al.
Publicado: (2025)
Dissipative Preparation of Many-Body Quantum States: Towards Practical Quantum Advantage
por: Lin, Lin
Publicado: (2025)
por: Lin, Lin
Publicado: (2025)
A Hierarchical Approach to Quantum Many-Body Systems in Structured Environments
por: Müller, Kai, et al.
Publicado: (2024)
por: Müller, Kai, et al.
Publicado: (2024)
Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
por: Venturella, Christian, et al.
Publicado: (2024)
por: Venturella, Christian, et al.
Publicado: (2024)
Design, Assessment, and Application of Machine Learning Potential Energy Surfaces
por: Andreichev, Valerii, et al.
Publicado: (2025)
por: Andreichev, Valerii, et al.
Publicado: (2025)
Variational Benchmarks for Quantum Many-Body Problems
por: Wu, Dian, et al.
Publicado: (2023)
por: Wu, Dian, et al.
Publicado: (2023)
Quantum State Tomography using Quantum Machine Learning
por: Innan, Nouhaila, et al.
Publicado: (2023)
por: Innan, Nouhaila, et al.
Publicado: (2023)
Quantum Many-Body Simulations of Catalytic Metal Surfaces
por: Cao, Changsu, et al.
Publicado: (2025)
por: Cao, Changsu, et al.
Publicado: (2025)
Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems
por: Wilson, Blake A., et al.
Publicado: (2021)
por: Wilson, Blake A., et al.
Publicado: (2021)
Extracting Many-Body Quantum Resources within One-Body Reduced Density Matrix Functional Theory
por: Benavides-Riveros, Carlos L., et al.
Publicado: (2023)
por: Benavides-Riveros, Carlos L., et al.
Publicado: (2023)
Advancing Natural Orbital Functional Calculations Through Deep Learning-Inspired Techniques for Large-Scale Strongly Correlated Electron Systems
por: Lew-Yee, Juan Felipe Huan, et al.
Publicado: (2024)
por: Lew-Yee, Juan Felipe Huan, et al.
Publicado: (2024)
Ab-Initio Approach to Many-Body Quantum Spin Dynamics
por: Dubey, Aditya, et al.
Publicado: (2024)
por: Dubey, Aditya, et al.
Publicado: (2024)
Many-Body Time Evolution from a Correlation-Efficient Quantum Algorithm
por: Rose, Michael, et al.
Publicado: (2025)
por: Rose, Michael, et al.
Publicado: (2025)
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
por: Beucler, Tom, et al.
Publicado: (2024)
por: Beucler, Tom, et al.
Publicado: (2024)
Variational Learning of Physical Intuition from a Few Observations
por: Peng, Jingruo, et al.
Publicado: (2025)
por: Peng, Jingruo, et al.
Publicado: (2025)
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
por: Liu, Xiaoqing, et al.
Publicado: (2025)
por: Liu, Xiaoqing, et al.
Publicado: (2025)
Renormalization-Inspired Effective Field Neural Networks for Scalable Modeling of Classical and Quantum Many-Body Systems
por: Liu, Xi, et al.
Publicado: (2025)
por: Liu, Xi, et al.
Publicado: (2025)
Exploring Cross-Modal Flows for Few-Shot Learning
por: Jiang, Ziqi, et al.
Publicado: (2025)
por: Jiang, Ziqi, et al.
Publicado: (2025)
Transformer Learning of Chaotic Collective Dynamics in Many-Body Systems
por: Jang, Ho, et al.
Publicado: (2026)
por: Jang, Ho, et al.
Publicado: (2026)
Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
por: Deng, Ken, et al.
Publicado: (2026)
por: Deng, Ken, et al.
Publicado: (2026)
Exploring Nonlinear System with Machine Learning: Chua and Lorenz Circuits Analyzed
por: Wang, Zhe, et al.
Publicado: (2024)
por: Wang, Zhe, et al.
Publicado: (2024)
CERTIFY-ED: A Multi-Layer Verification Framework for Exact Diagonalization of Quantum Many-Body Systems
por: Vehale, Sarang, et al.
Publicado: (2026)
por: Vehale, Sarang, et al.
Publicado: (2026)
Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
por: Setty, Abhishek, et al.
Publicado: (2023)
por: Setty, Abhishek, et al.
Publicado: (2023)
Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems
por: Wei, Lu, et al.
Publicado: (2025)
por: Wei, Lu, et al.
Publicado: (2025)
Ejemplares similares
-
The Steepest Slope toward a Quantum Few-body Solution: Gradient Variational Methods for the Quantum Few-body Problem
por: Recchia, Paolo, et al.
Publicado: (2024) -
Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions
por: Chen, Ying, et al.
Publicado: (2025) -
SAFE Quantum Machine Learning with Variational Quantum Classifiers
por: Chen, Ying, et al.
Publicado: (2026) -
Variational Quantum Algorithms for Many-Body Systems
por: Consiglio, Mirko
Publicado: (2025) -
Variational Embeddings for Many Body Quantum Systems
por: Barison, Stefano, et al.
Publicado: (2023)