1 citations · 1 across the 3 of their papers we have counts for
6 papers · 1 filter
A Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization
Vojtěch Novák, Tomáš Bezděk, 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
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…
Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study
Vojtěch Novák, Ivan Zelinka, Lenka Přibylová +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
Vojtěch Novák, Ivan Zelinka, Lenka Přibylová +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…
Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Vojtěch 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…