8 papers
Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution
VojtÄch Novák, Ivan Zelinka
Swarm and evolutionary algorithms are usually analyzed as complete procedural systems in which nonlinear selection, replacement, and adaptation obscure simpler structure within can…
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
VojtÄch Novák, Ivan Zelinka, Lenka PÅibylová +3
This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14\%…
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…