Cover Image

Lindbladian Learning with Neural Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heightman, Timothy, Gallo, Roman Aseguinolaza, Jiang, Edward, Saavedra, JRM, Acín, Antonio, Płodzień, Marcin
Format:	Preprint
Published:	2026
Subjects:	Quantum Physics Disordered Systems and Neural Networks Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural quantum states for entanglement depth certification from randomized Pauli measurements
by: Płodzień, Marcin
Published: (2025)

Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
by: Cybiński, Kacper, et al.
Published: (2024)

Natural Quantization of Neural Networks
by: Barney, Richard, et al.
Published: (2025)

Localization of Lindbladian Fermions
by: Thompson, Foster, et al.
Published: (2024)

Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware
by: Lakhdar-Hamina, Djamil, et al.
Published: (2025)

Unlearnable phases of matter
by: Kumar, Tarun Advaith, et al.
Published: (2026)

Variational Tensor Neural Networks for Deep Learning
by: Jahromi, Saeed S., et al.
Published: (2022)

Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equations
by: Heightman, Timothy, et al.
Published: (2024)

Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
by: Ramezanpour, Abolfazl
Published: (2025)

How Feature Learning Can Improve Neural Scaling Laws
by: Bordelon, Blake, et al.
Published: (2024)

Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
by: Dandi, Yatin, et al.
Published: (2026)

Statistical Physics of Deep Neural Networks: Generalization Capability, Beyond the Infinite Width, and Feature Learning
by: Ariosto, Sebastiano
Published: (2025)

The Quantization Model of Neural Scaling
by: Michaud, Eric J., et al.
Published: (2023)

Formation of Representations in Neural Networks
by: Ziyin, Liu, et al.
Published: (2024)

Explaining Neural Scaling Laws
by: Bahri, Yasaman, et al.
Published: (2021)

Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures
by: Cagnetta, Francesco, et al.
Published: (2025)

Geometric quantum machine learning of BQP$^A$ protocols and latent graph classifiers
by: Umeano, Chukwudubem, et al.
Published: (2024)

Generalized Probabilistic Approximate Optimization Algorithm
by: Abdelrahman, Abdelrahman S., et al.
Published: (2025)

A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
by: Hibat-Allah, Mohamed, et al.
Published: (2023)

Quantum Equilibrium Propagation: Gradient-Descent Training of Quantum Systems
by: Scellier, Benjamin
Published: (2024)

Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
by: Venturelli, Francesco Aldo, et al.
Published: (2024)

Physics-inspired transformer quantum states via latent imaginary-time evolution
by: Yamazaki, Kimihiro, et al.
Published: (2026)

What can we learn from quantum convolutional neural networks?
by: Umeano, Chukwudubem, et al.
Published: (2023)

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks
by: Dao, H. L.
Published: (2026)

Application of time-series quantum generative model to financial data
by: Okumura, Shun, et al.
Published: (2024)

Neural Scaling Laws Rooted in the Data Distribution
by: Brill, Ari
Published: (2024)

Graph Neural Networks Do Not Always Oversmooth
by: Epping, Bastian, et al.
Published: (2024)

A Dynamical Model of Neural Scaling Laws
by: Bordelon, Blake, et al.
Published: (2024)

Variational Neural Annealing
by: Hibat-Allah, Mohamed, et al.
Published: (2021)

Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality
by: Pacifico, Feliciano Giuseppe, et al.
Published: (2026)

Demolition and Reinforcement of Memories in Spin-Glass-like Neural Networks
by: Ventura, Enrico
Published: (2024)

Growing Neural Networks: Dynamic Evolution through Gradient Descent
by: Radhakrishnan, Anil, et al.
Published: (2025)

The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks
by: Farné, Gabriele, et al.
Published: (2026)

Dynamical Mean-Field Theory of Self-Attention Neural Networks
by: Poc-López, Ángel, et al.
Published: (2024)

Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
by: Skenderi, Geri, et al.
Published: (2026)

A Random-Matrix Criterion for Initializing Gated Recurrent Neural Networks
by: Fioratti, Tommaso, et al.
Published: (2026)

Controlled Langevin Dynamics for Sampling of Feedforward Neural Networks Trained with Minibatches
by: Zambon, Alessandro, et al.
Published: (2026)

A Federated Many-to-One Hopfield model for associative Neural Networks
by: Alessandrelli, Andrea, et al.
Published: (2026)

Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning
by: Williams, Chelsea A., et al.
Published: (2024)

Probabilistic Computers for Neural Quantum States
by: Chowdhury, Shuvro, et al.
Published: (2025)