1 citations · 1 across the 16 of their papers we have counts for
21 papers
On Embedding Design in Quantum Physics-Informed Neural Networks
Ban Q. Tran, Nahid Binandeh Dehaghani, Susan Mengel +2
Quantum physics-informed neural networks (QPINNs) solve partial differential equations (PDEs) by training parameterized quantum circuits against physics-based residuals, yet the ro…
Rank-Dependent Error Bounds and Near-Optimality in Quantum Control via Hierarchical Tucker Surrogates
Nahid Binandeh Dehaghani, Rafal Wisniewski, Julian Berberich +1
We develop a certified finite-horizon quantum optimal-control framework based on fixed-rank Hierarchical Tucker (HT) surrogates. Under a uniform rank-dependent HT truncation-accura…
Tensor Network Methods for Advection-Diffusion-Reaction Systems Using Quantum-Inspired Representations
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a quantum-inspired tensor-network framework for solving advection-diffusion-reaction (ADR) partial differential equations. Discretized solution fields are encoded as mat…
A Variational Surrogate Approach to Finite-Horizon Quantum Control via Hardware-Efficient Ansatz
Nahid Binandeh Dehaghani, Rafal Wisniewski, A. Pedro Aguiar
We present a variational quantum framework for finite-horizon quantum control based on hardware-efficient ansätze. The objective is to steer a quantum system from a given initial s…
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…
Investigation of Feature Selection and Pooling Methods for Environmental Sound Classification
Parinaz Binandeh Dehaghani, Danilo Pena, A. Pedro Aguiar
This paper explores the impact of dimensionality reduction and pooling methods for Environmental Sound Classification (ESC) using lightweight CNNs. We evaluate Sparse Salient Regio…
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 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…
Robust Entanglement Generation in Bipartite Quantum Systems Using Optimal Control
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
Quantum entanglement is a key resource for quantum technologies, yet its efficient and high-fidelity generation remains a challenge due to the complexity of quantum dynamics. This…
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…
State-Constrained Optimal Control for Coherence Preservation in Multi-Level Open Quantum Systems
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
This paper addresses the optimal control of quantum coherence in multi-level systems, modeled by the Lindblad master equation, which captures both unitary evolution and environment…
A Hybrid Quantum-Classical Physics-Informed Neural Network Architecture for Solving Quantum Optimal Control Problems
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
This paper proposes an integrated quantum-classical approach that merges quantum computational dynamics with classical computing methodologies tailored to address control problems…
Optimizing Maximally Entangled State Generation via Pontryagin's Principle
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
We propose an optimal control strategy to generate maximally entangled states in bipartite quantum systems. Leveraging the Pontryagin Principle, we derive time-dependent control fi…
State Estimation and Control for Stochastic Quantum Dynamics with Homodyne Measurement: Stabilizing Qubits under Uncertainty
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
This paper introduces a Lyapunov-based control approach with homodyne measurement. We study two filtering approaches: (i) the traditional quantum filtering and (ii) a modified vers…
Exploring Strange Entanglement: Experimental and Theoretical Perspectives on Neutral Kaon Systems
Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski
This chapter provides an in-depth analysis of the properties and phenomena associated with neutral K-mesons. Kaons are quantum systems illustrating strange behaviours. We begin by…
An Application of Pontryagin Neural Networks to Solve Optimal Quantum Control Problems
Nahid Binandeh Dehaghani, A. Pedro Aguiar
Reliable high-fidelity quantum state transformation has always been considered as an inseparable part of quantum information processing. In this regard, Pontryagin maximum principl…
Quantum State Transfer Optimization: Balancing Fidelity and Energy Consumption using Pontryagin Maximum Principle
Nahid Binandeh Dehaghani, A. Pedro Aguiar
In this study, we address a control-constrained optimal control problem pertaining to the transformation of quantum states. Our objective is to navigate a quantum system from an in…
A Quantum Optimal Control Problem with State Constrained Preserving Coherence
Nahid Binandeh Dehaghani, Fernando Lobo Pereira
In this work, we address the problem of maximizing fidelity in a quantum state transformation process controlled in such a way as to keep decoherence within given bounds. We consid…
High Fidelity Quantum State Transfer by Pontryagin Maximum Principle
Nahid Binandeh Dehaghani, Fernando Lobo Pereira
High fidelity quantum state transfer is an essential part of quantum information processing. In this regard, we address the problem of maximizing the fidelity in a quantum state tr…