5 citations · 11 across the 11 of their papers we have counts for
11 papers
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…
How an Equi-ensemble Description Systematically Outperforms the Weighted-ensemble Variational Quantum Eigensolver
Akilan Rajamani, Martin Beseda, Benjamin Lasorne +1
Calculating excited states in chemistry is crucial to provide insight into photoinduced molecular behavior beyond the ground state, enabling innovations in spectroscopy, material s…
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…
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…
Transformation-free generation of a quasi-diabatic representation from the state-average orbital-optimized variational quantum eigensolver
Silvie Illésová, Martin Beseda, Saad Yalouz +2
In the present work, we examine how the recent quantum-computing algorithm known as the state-average orbital-optimized variational quantum eigensolver (SA-OO-VQE), viewed within t…
Detecting quasi-degenerate ground states in topological models via variational quantum eigensolver
Carola Ciaramelletti, Martin Beseda, Mirko Consiglio +3
We study the exact ground states of the Su--Schrieffer--Heeger open chain and of the Kitaev open chain, using the Variational Quantum Eigensolver (VQE) algorithm. These models host…
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…