4 citations · 7 across the 11 of their papers we have counts for
16 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…
Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction
Magnus Munk Jensen, Dorte Hammershøi, Rafał Wiśniewski +1
Fault detection is essential in industrial systems, enabling early identification of abnormal behaviour and improving safety, reliability, and operational efficiency. Modern system…
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…
Fault detection on manifolds of nonlinear dynamical systems with dual autoencoders
Bulut Kuşkonmaz, Szymon Greś, Rafał Wiśniewski
Autoencoders are commonly used for unsupervised data-driven fault detection in nonlinear dynamical systems. Despite their widespread success and often favorable performance compare…
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-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…
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…
Weighted Feedback-Based Quantum Algorithm for Excited States Calculation
Salahuddin Abdul Rahman, Özkan Karabacak, Rafal Wisniewski
Drawing inspiration from the Lyapunov control technique for quantum systems, feedback-based quantum algorithms have been proposed for calculating the ground states of Hamiltonians.…
Feedback-Based Quantum Algorithm for Excited States Calculation
Salahuddin Abdul Rahman, Özkan 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…
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…
Online Parameter Estimation for Continuously Monitored Quantum Systems
Henrik Glavind Clausen, Pierre Rouchon, Rafal Wisniewski
In this work, we consider the problem of online (real-time, single-shot) estimation of static or slow-varying parameters along quantum trajectories in quantum dynamical systems. Ba…