1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
A Kernel-Based Approach for Accurate Steady-State Detection in Performance Time Series
Martin Beseda, Vittorio Cortellessa, Daniele Di Pompeo +2
This paper addresses the challenge of accurately detecting the transition from the warmup phase to the steady state in performance metric time series, which is a critical step for…
Benchmarking gate-based quantum devices via certification of qubit von Neumann measurements
Paulina Lewandowska, Martin Beseda
We present an updated version of PyQBench, an open-source Python library designed for benchmarking gate-based quantum computers, with a focus on certifying qubit von Neumann measur…
A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection
Antonio Trovato, Martin Beseda, Dario Di Nucci
Regression testing is key in verifying that software works correctly after changes. However, running the entire regression test suite can be impractical and expensive, especially f…