most citedA Longitudinal Analysis of the CEC Single-Objective Competitions (2010-2024) and Implications for Variational Quantum Optimization

1 citations · 1 across the 8 of their papers we have counts for

collaborators

13 papers

quant-ph2026

Optimization Geometry of QAOA and Variational Quantum Algorithms

Vojtěch Novák, Ivan Zelinka, Silvie Illésová +2

Variational quantum algorithms turn choices of Hamiltonian, ansatz, and parameterization into a classical nonconvex optimization problem. We study how this objective function can b…

cs.NE2026

Adaptive Rotation for iSOMA: Geometry, Benchmarking, and Noise Robustness in Variational Quantum Objectives

Vojtěch Novák, Ivan Zelinka

We study whether the coordinate dependence of the improved Self-Organizing Migrating Algorithm (iSOMA) can be reduced while retaining its inexpensive leader-directed migration mech…

quant-ph2026

Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization

Vojtěch Novák, Ivan Zelinka, Swagatam Das +1

We benchmark classical optimization of a fixed low-depth Quantum Approximate Optimization Algorithm (QAOA) ansatz across four cost-Hamiltonian families at , , and .…

quant-ph2026

When is global evolutionary search useful for variational quantum algorithms? A landscape-first study

Vojtěch Novák, Ivan Zelinka

Variational quantum algorithms recast state preparation as classical nonconvex optimization, but it is often unclear when multistart local search suffices and when population-based…

quant-ph2026

Optimization Landscape Geometry in VQE for Frustrated Quantum Spin Models

Vojtěch Novák, Ivan Zelinka, Swagatam Das +1

We benchmark eight classical optimizers for exact-statevector VQE calculations on a controlled hierarchy of frustrated spin models, ranging from a diagonal Ising glass to transvers…

cs.NE2026

Linear Proposal Operators and Stochastic Search Geometry in SOMA and Differential Evolution

Vojtěch 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…

cs.LG2026

Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors

Vojtěch Novák, Ivan Zelinka, Lenka Přibylová +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\%…

quant-ph2026★ 1 cited

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…

quant-ph2025

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…

quant-ph2025

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…

quant-ph2025

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…

quant-ph2025

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…

quant-ph2025

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…