From the 2 of 8 linked papers with an AI index.
8 papers
Tensor Network Methods for Advection-Diffusion-Reaction Systems Using Quantum-Inspired Representations
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
The paper introduces a quantum-inspired tensor‑network approach that encodes discretized advection‑diffusion‑reaction PDE solutions as matrix product states and operators, enabling…
A Variational Surrogate Approach to Finite-Horizon Quantum Control via Hardware-Efficient Ansatz
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
The paper introduces a variational quantum method that uses a hardware-efficient parameterized circuit as a surrogate to achieve finite-horizon state-transfer control, optimizing c…
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…
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…
Quantum-Assisted Trainable-Embedding Physics-Informed Neural Networks for Parabolic PDEs
Ban Q. Tran, Nahid Binandeh Dehaghani, Rafal Wisniewski +2
Physics-informed neural networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding governing physical laws directly into t…
Feedback-Based Quantum Algorithm for Excited States Calculation
Salahuddin Abdul Rahman, Ãzkan Karabacak, Rafal Wisniewski
Recently, feedback-based quantum algorithms have been introduced to calculate the ground states of Hamiltonians, inspired by quantum Lyapunov control theory. This paper aims to gen…