4 papers · 1 filter
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…
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…
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 Solution Framework for Finite-Horizon LQG Control via Block Encodings and QSVT
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a quantum algorithm for solving the finite-horizon discrete-time Linear Quadratic Gaussian (LQG) control problem, which integrates optimal control and state estimation i…