1 citations · 1 across the 9 of their papers we have counts for
16 papers
Hybrid Quantum-Classical Optimization for Emergency-Priority Traffic Signal Control
Silvie Illésová, Vojtěch Novák
Emergency-priority traffic signal control requires reducing emergency-vehicle delay while limiting disruption to ordinary traffic. This work investigates a finite-horizon QUBO form…
Reducing QUBO Complexity in Drone Delivery Through Classical Feasibility Preprocessing
Silvie Illésová, Vojtěch Novák, Martin Beseda
Drone-delivery planning combines routing decisions with operational constraints such as payload capacity and limited onboard energy, resulting in combinatorial optimization problem…
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…
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…
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 .…
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…
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…
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…
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\%…
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…
Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency
Tomáš Bezděk, Haomu Yuan, Vojtěch 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 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…
Numerical Optimization Strategies for the Variational Hamiltonian Ansatz in Noisy Quantum Environments
S. Illésová, V. Novák, T. Bezděk +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…