5 papers
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…
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…
Leveraging Quantum Layers in Classical Neural Networks
Silvie Illésová
Hybrid quantum-classical neural networks represent a promising frontier in the search for improved machine learning models. This thesis explores the integration of quantum layers w…
QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions
Silvie Illésová, Tomasz Rybotycki, Martin Beseda
As hybrid quantum-classical models gain traction in machine learning, there is a growing need for tools that assess their effectiveness beyond raw accuracy. We present QMetric, a P…