9 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…
Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
S. Illésová, V. Novák, T. BezdÄk +2
The prevalence of variational methods in near-term quantum computing makes optimizer choice critical, yet selection is frequently intuition-based. We therefore present a systematic…
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…
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…