From the 2 of 8 linked papers with an AI index.
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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-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…
Trotterized Variational Quantum Control for Spin-Chain State Transfer
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a hybrid variational framework for quantum optimal control aimed at high-fidelity state transfer in spin chains. The system dynamics are discretized and compiled into a…
Quantum-Assisted Barrier Sequential Quadratic Programming for Nonlinear Optimal Control
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We propose a quantum-assisted framework for solving constrained finite-horizon nonlinear optimal control problems using a barrier Sequential Quadratic Programming (SQP) approach. W…