4 citations · 4 across the 9 of their papers we have counts for
12 papers
Hybrid Quantum-Classical Optimization for Emergency-Priority Traffic Signal Control
Silvie Illésová, Vojtěch Novák
Emergency-priority traffic signal control requires reducing emergency-vehicle delay while limiting disruption to ordinary traffic. This work investigates a finite-horizon QUBO form…
Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing
Silvie Illésová, Vojtěch Novák, Martin Beseda
Drone-delivery planning combines routing decisions with operational constraints such as payload capacity and limited onboard energy, resulting in combinatorial optimization problem…
Optimization Geometry of QAOA and Variational Quantum Algorithms
Vojtěch Novák, Ivan Zelinka, Silvie Illésová +2
Variational quantum algorithms turn choices of Hamiltonian, ansatz, and parameterization into a classical nonconvex optimization problem. We study how this objective function can b…
A Semantic Framework for Reproducible Variational Quantum Algorithm Execution Records
Silvie Illésová, Martin Beseda
Variational quantum algorithms are hybrid quantum-classical workflows whose results depend on many interacting choices, including the ansatz, Hamiltonian, optimizer, backend, shot…
Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency
Tomáš Bezděk, Haomu Yuan, Vojtěch Novák +2
This study systematically benchmarks classical optimization strategies for the Quantum Approximate Optimization Algorithm when applied to Generalized Mean-Variance Problems under n…
Reliable Optimization Under Noise in Quantum Variational Algorithms
Vojtěch Novák, Silvie Illésová, Tomáš Bezděk +2
The optimization of Variational Quantum Eigensolver is severely challenged by finite-shot sampling noise, which distorts the cost landscape, creates false variational minima, and i…
From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning
Silvie Illésová, Tomáš Bezděk, Vojtěch Novák +3
This work addresses the challenge of enabling practitioners without quantum expertise to transition from classical to hybrid quantum-classical machine learning workflows. We propos…
Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
Silvie Illésová, Tomáš Bezděk, Vojtěch Novák +2
This work investigates the performance of numerical optimization algorithms applied to the State-Averaged Orbital-Optimized Variational Quantum Eigensolver for the H2 molecule unde…
Leveraging Quantum Layers in Classical Neural Networks
Silvie Illésová
Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers w…
QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions
Silvie Illésová, Tomasz Rybotycki, Martin Beseda
As hybrid quantum-classical models gain traction in machine learning, there is a growing need for tools that assess their effectiveness beyond raw accuracy. We present QMetric, a P…
On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models
Silvie Illésová, Tomasz Rybotycki, Piotr Gawron +1
We present a systematic study of how quantum circuit design, specifically the depth of the variational ansatz and the choice of quantum feature mapping, affects the performance of…
State-Averaged Orbital-Optimized VQE: A quantum algorithm for the democratic description of ground and excited electronic states
Martin Beseda, Silvie Illésová, Saad Yalouz +1
The electronic structure problem is one of the main problems in modern theoretical chemistry. While there are many already-established methods both for the problem itself and its a…