6 papers
A QPINN Framework with Quantum Trainable Embeddings for the Lid-Driven Cavity Problem
Nahid Binandeh Dehaghani, Ban Q. Tran, Susan Mengel +2
The steady incompressible Navier--Stokes equations pose significant computational challenges due to their nonlinear convective terms and pressure--velocity coupling. Physics-inform…
A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks
Ban Q. Tran, Duong M. Chu, Hai T. D. Pham +3
Quantum Machine Learning (QML) has recently emerged as a highly promising research frontier. Within this domain, Quantum Neural Networks (QNNs),characterized by Variational Quantum…
Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Ban Q. Tran, Chuong K. Luong, Viet Q. Nguyen +2
Machine learning models and their applications, such as autonomous driving systems, are becoming increasingly common and are essential components of human daily life. However, due…
Quantum-Inspired Tensor Networks for Approximating PDE Flow Maps
Nahid Binandeh Dehaghani, Ban Q. Tran, Rafal Wisniewski +2
We investigate quantum-inspired tensor networks (QTNs) for approximating flow maps of hydrodynamic partial differential equations (PDEs). Motivated by the effective low-rank struct…
A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems
Ban Q. Tran, Nahid Binandeh Dehaghani, A. Pedro Aguiar +2
Physics-informed neural networks (PINNs) and hybrid quantum-classical extensions provide a promising framework for solving partial differential equations (PDEs) by embedding physic…
Quantum-Assisted Learning of Time-Dependent Parabolic PDEs
Nahid Binandeh Dehaghani, Ban Tran, A. Pedro Aguiar +2
We present a hybrid quantum-classical framework for solving general time-dependent parabolic partial differential equations (PDEs) using quantum variational circuits. Building on t…