6 papers · 1 filter
A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization
VojtÄch Novák, Tomáš BezdÄk, Ivan Zelinka +2
This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010-2024). We analyze how benchmark functions shaped winning algorithm…
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…
Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
VojtÄch Novák, Ivan Zelinka, Václav Snášel
Variational Quantum Algorithms (VQAs) are a leading approach for near-term quantum computing but face major optimization challenges from noise, barren plateaus, and complex energy…
Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study
VojtÄch Novák, Ivan Zelinka, Lenka PÅibylová +2
Anastomotic leak (AL) is a life-threatening complication following colorectal surgery, and its accurate prediction remains a significant clinical challenge. This study explores the…
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
VojtÄch Novák, Ivan Zelinka, Lenka PÅibylová +1
This study investigates the application of quantum neural networks (QNNs) for propensity score estimation to address selection bias in comparing survival outcomes between laparosco…