Author

Nahid Binandeh Dehaghani

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Preprint Sep 2026

On Embedding Design in Quantum Physics-Informed Neural Networks

Quantum physics-informed neural networks (QPINNs) solve partial differential equations (PDEs) by training parameterized quantum circuits against physics-based residuals, yet the role of the embedding that maps coordinates into quantum states remains insufficiently understood. In this research, we introduce a unified em...

B. Tran, Nahid Binandeh Dehaghani, Susan A. Mengel et al. · 0 citations

Preprint Jul 2026

A Variational Surrogate Approach to Finite-Horizon Quantum Control via Hardware-Efficient Ansatz

A variational quantum framework for finite-horizon quantum control based on hardware-efficient ans\"atze, providing a flexible and implementation-friendly approach compatible with near-term quantum devices.

Nahid Binandeh Dehaghani, Rafał Wiśniewski, A. Aguiar · 0 citations

Conference Open access Jul 2026

Tensor Network Methods for Advection–Diffusion–Reaction Systems Using Quantum-Inspired Representations

A quantum-inspired tensor-network framework for solving advection–diffusion–reaction (ADR) partial differential equations and results highlight the potential of tensor networks as efficient structure-preserving tools for PDE simulation in multiple spatial dimensions.

N. Dehaghani, Rafał Wiśniewski, A. Aguiar · International Conference on... · 0 citations