Cover Image

Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yuan, Khaliq, Abdul, Furati, Khaled M.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Numerical Analysis Quantum Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
by: Kim, Chanyoung, et al.
Published: (2026)

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)

Fast and Flexible Quantum-Inspired Differential Equation Solvers with Data Integration
by: Arenstein, Lucas, et al.
Published: (2025)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
by: Sun, Jingbo, et al.
Published: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
by: Mehmood, Muhammed Ali, et al.
Published: (2026)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
by: Gorard, Jonathan, et al.
Published: (2026)

Partially Unitary Learning
by: Belov, Mikhail Gennadievich, et al.
Published: (2024)

Variational Quantum Framework for Partial Differential Equation Constrained Optimization
by: Surana, Amit, et al.
Published: (2024)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
by: Katende, Ronald
Published: (2025)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
by: Zhou, Wei, et al.
Published: (2024)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
by: Negi, Pawan, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
by: Hao, Wenrui, et al.
Published: (2024)

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
by: Mahowald, Jamie, et al.
Published: (2026)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
by: Gonon, Lukas, et al.
Published: (2024)

Quantum Implicit-Explicit Schemes for Multiscale Ordinary and Partial Differential Equations via Schrödingerization
by: Hu, Qitong, et al.
Published: (2026)

Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
by: Lopez-Piqueres, Javier, et al.
Published: (2024)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
by: Rao, Xiang, et al.
Published: (2025)

Quantum Channel Learning
by: Belov, Mikhail Gennadievich, et al.
Published: (2024)

Randomized Neural Networks for Integro-Differential Equations with Application to Neutron Transport
by: Dang, Haoning, et al.
Published: (2026)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

On Encoding Matrices using Quantum Circuits
by: Yosef, Liron Mor, et al.
Published: (2025)

Semidefinite Programming for Quantum Channel Learning
by: Belov, Mikhail Gennadievich, et al.
Published: (2026)

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
by: Dai, Chen-Yang, et al.
Published: (2026)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
by: Mialon, Grégoire, et al.
Published: (2023)

In Situ Quantum Analog Pulse Characterization via Structured Signal Processing
by: Dong, Yulong, et al.
Published: (2025)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
by: Ma, Qinglong, et al.
Published: (2024)

Qubit-Efficient Quantum Algorithm for Linear Differential Equations
by: Fang, Di, et al.
Published: (2025)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
by: Liang, Senwei, et al.
Published: (2022)

High-Resolution Tensor-Network Fourier Methods for Exponentially Compressed Non-Gaussian Aggregate Distributions
by: Rodríguez-Aldavero, Juan José, et al.
Published: (2026)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
by: Hammad, M. M.
Published: (2026)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
by: Nagy-Huber, Monika, et al.
Published: (2023)

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
by: Mikuš, Iva, et al.
Published: (2026)