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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 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…
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…
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…
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…