7 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…
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…
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…