arXiv subjects

Tomáš Bezděk

Publications and source records attributed to Tomáš Bezděk.

7 recordsLinked to original sources

arXiv2026

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

This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010-2024). We analyze how benchmark functions shaped winning algorithms, identifying the 2014 introduction of dense rotation matrices as a key performance filter. This design choice introduced parameter non-separability, reduced effectiveness of coordinate-dependent methods (PSO, GA), and established the dominance of Differential Evolution variants capable of preserving the rotational invariance of their difference vectors, specifically L-SHADE. Post-2020 analysis reveals a shift towards high complexity hybrid optimizers that combine different mechanisms (e.g., Eigenvector Crossover, Societal Sharing, Reinforcement Learning) to maximize ranking stability. We conclude by identifying structural similarities between these modern benchmarks and Variational Quantum Algorithm landscapes, suggesting that evolved CEC solvers possess the specific adaptive capabilities required for quantum control.

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arXiv2026

Statistical Benchmarking of Optimization Methods for Variational Quantum Eigensolver under Quantum Noise

This work investigates the performance of numerical optimization algorithms applied to the State-Averaged Orbital-Optimized Variational Quantum Eigensolver for the H2 molecule under various quantum noise conditions. The goal is to assess the stability, accuracy, and computational efficiency of commonly used gradient-based, gradient-free, and global optimization strategies within the Noisy Intermediate-Scale Quantum regime. We systematically compare six representative optimizers, BFGS, SLSQP, Nelder-Mead, Powell, COBYLA, and iSOMA, under ideal, stochastic, and decoherence noise models, including phase damping, depolarizing, and thermal relaxation channels. Each optimizer was tested over multiple noise intensities and measurement settings to characterize convergence behavior and sensitivity to noise-induced landscape distortions. The results show that BFGS consistently achieves the most accurate energies with minimal evaluations, maintaining robustness even under moderate decoherence. COBYLA performs well for low-cost approximations, while SLSQP exhibits instability in noisy regimes. Global approaches such as iSOMA show potential but are computationally expensive. These findings provide practical guidance for selecting suitable optimizers in variational quantum simulations, highlighting the importance of noise-aware optimization strategies for reliable and efficient quantum chemistry computations on current hardware.

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arXiv2025

Classical Optimization Strategies for Variational Quantum Algorithms: A Systematic Study of Noise Effects and Parameter Efficiency

This study systematically benchmarks classical optimization strategies for the Quantum Approximate Optimization Algorithm when applied to Generalized Mean-Variance Problems under near-term Noisy Intermediate-Scale Quantum conditions. We evaluate Dual Annealing, Constrained Optimization by Linear Approximation, and the Powell Method across noiseless, sampling noise, and two thermal noise models. Our Cost Function Landscape Analysis revealed that the Quantum Approximate Optimization Algorithm angle parameters $γ$ were largely inactive in the noiseless regime. This insight motivated a parameter-filtered optimization approach, in which we focused the search space exclusively on the active $β$ parameters. This filtering substantially improved parameter efficiency for fast optimizers like Constrained Optimization by Linear Approximation (reducing evaluations from 21 to 12 in the noiseless case) and enhanced robustness, demonstrating that leveraging structural insights is an effective architecture-aware noise mitigation strategy for Variational Quantum Algorithms.

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arXiv2025

From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning

This work addresses the challenge of enabling practitioners without quantum expertise to transition from classical to hybrid quantum-classical machine learning workflows. We propose a three-stage framework: starting with a classical self-training model, then introducing a minimal hybrid quantum variant, and finally applying diagnostic feedback via QMetric to refine the hybrid architecture. In experiments on the Iris dataset, the refined hybrid model improved accuracy from 0.31 in the classical approach to 0.87 in the quantum approach. These results suggest that even modest quantum components, when guided by proper diagnostics, can enhance class separation and representation capacity in hybrid learning, offering a practical pathway for classical machine learning practitioners to leverage quantum-enhanced methods.

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arXiv2025

Reliable Optimization Under Noise in Quantum Variational Algorithms

The optimization of Variational Quantum Eigensolver is severely challenged by finite-shot sampling noise, which distorts the cost landscape, creates false variational minima, and induces statistical bias called winner's curse. We investigate this phenomenon by benchmarking eight classical optimizers spanning gradient-based, gradient-free, and metaheuristic methods on quantum chemistry Hamiltonians H$_2$, H$_4$ chain, LiH (in both full and active spaces) using the truncated Variational Hamiltonian Ansatz. We analyze difficulties of gradient-based methods (e.g., SLSQP, BFGS) in noisy regimes, where they diverge or stagnate. We show that the bias of estimator can be corrected by tracking the \textit{population mean}, rather than the biased best individual when using population based optimizer. Our findings, which are shown to generalize to hardware-efficient circuits and condensed matter models, identify adaptive metaheuristics (specifically CMA-ES and iL-SHADE) as the most effective and resilient strategies. We conclude by presenting a set of practical guidelines for reliable VQE optimization under noise, centering on the co-design of physically motivated ansatz and the use of adaptive optimizers.

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arXiv2025

Numerical Optimization Methods in the environment with Quantum Noise

The accurate calculation of electronic potential energy surfaces for ground and excited states is crucial for understanding photochemical processes, particularly near conical intersections. While classical methods are limited by scaling and quantum algorithms by hardware, this thesis focuses on the State-Averaged Orbital-Optimized Variational Quantum Eigensolver (SA-OO-VQE). This hybrid quantum-classical algorithm provides a balanced description of multiple electronic states by combining quantum state preparation with classical state-averaged orbital optimization. A key contribution is the implementation and evaluation of the Differential Evolution algorithm within the SA-OO-VQE framework, with a comparative study against classical optimizers like the Broyden-Fletcher-Goldfarb-Shanno (BFGS) and Sequential Least Squares Programming (SLSQP) algorithms. The performance of these optimizers is assessed by calculating ground and first excited state energies for H$_2$, H$_4$, and LiH. The thesis also demonstrates SA-OO-VQE's capability to accurately model potential energy surfaces near conical intersections, using formaldimine as a case study. The results show that orbital optimization is essential for correctly capturing the potential energy surface topology, a task where standard methods with fixed orbitals fail. Our findings indicate that while Differential Evolution presents efficiency challenges, gradient-based methods like BFGS and SLSQP offer superior performance, confirming that the SA-OO-VQE approach is crucial for treating complex electronic structures.

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arXiv2025

Experimental study of multiple-shot unitary channels discrimination using the IBM Q computers

Tasks involving black boxes appear frequently in quantum computer science. An example that has been deeply studied is quantum channel discrimination. In this work, we study the discrimination between two quantum unitary channels in the multiple-shot scenario. We challenge the theoretical results concerning the probability of correct discrimination with the results collected from experiments performed on the IBM Quantum processor Brisbane. Our analysis shows that neither too deep quantum circuits nor circuits that create too much entanglement are suitable for the discrimination task. We conclude that circuit architectures which minimize entanglement overhead while preserving discrimination power are significantly more resilient to hardware noise if their depth does not overpass threshold value.

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