Prehliadanie podľa Autor "Pivoluska, Matej"

Teraz sa zobrazuje 1 - 2 z 2
  • Obrázok miniatúry
    Položka
    Harmonic oscillator based particle swarm optimization
    (Public Library of Science : San Francisco, 2025) Chernyak, Yury; Mohammad, Ijaz Ahamed; Masnicak, Nikolas; Pivoluska, Matej; Plesch, Martin
    Numerical optimization techniques are widely applied across various fields of science and technology, ranging from determining the minimal energy of systems in physics and chemistry to identifying optimal routes in logistics or strategies for high-speed trading. Here, we present a novel method that integrates particle swarm optimization (PSO), a highly effective and widely used algorithm inspired by the collective behavior of bird flocks searching for food, with the physical principle of conserving energy and damping in harmonic oscillators. This physics-based approach allows smoother convergence throughout the optimization process and wider tunability options. We evaluated our method on a standard set of test functions and demonstrated that, in most cases, it outperforms its natural competitors, including the original PSO, as well as commonly used optimization methods such as COBYLA and Differential Evolution.
  • Obrázok miniatúry
    Položka
    Meta-optimization of resources on quantum computers
    (Nature Publishing Group : London, 2025) Mohammad Ijaz, Ahamed; Pivoluska, Matej; Plesch, Martin
    The current state of quantum computing is commonly described as the Noisy Intermediate-Scale Quantum era. Available computers contain a few dozens of qubits and can perform a few dozens of operations before the inevitable noise erases all information encoded in the calculation. Even if the technology advances fast within the next years, any use of quantum computers will be limited to short and simple tasks, serving as subroutines of more complex classical procedures. Even for these applications the resource efficiency, measured in the number of quantum computer runs, will be a key parameter. Here we suggest a general meta-optimization procedure for hybrid quantum-classical algorithms that allows finding the optimal approach with limited quantum resources. This method optimizes the usage of resources of an existing method by testing its capabilities and setting the optimal resource utilization. We demonstrate this procedure on a specific example of variational quantum algorithm used to find the ground state energy of a hydrogen molecule.