10 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…
Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise
Silvie Illésová, Tomáš BezdÄk, VojtÄch 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…
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…
Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency
Tomáš BezdÄk, Haomu Yuan, VojtÄch 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
VojtÄch Novák, Silvie Illésová, Tomáš BezdÄk +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 BezdÄk, VojtÄch 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…