Prehliadanie podľa Autor "Mohammad, Ijaz Ahamed"

Teraz sa zobrazuje 1 - 2 z 2
  • Obrázok miniatúry
    Položka
    Efficient implementation of single particle Hamiltonians in exponentially reduced qubit space
    (Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften : Viedeň, 2026) Plesch, Martin; Friák, Martin; Mohammad, Ijaz Ahamed
    Current and near-term quantum hardware is constrained by limited qubit counts, circuit depth, and the high cost of repeated measurements. We address these challenges for solid-state Hamiltonians by introducing a logarithmic-qubit encoding that maps a system with N physical sites onto only ⌈log2N⌉ qubits while maintaining a clear correspondence with the underlying physical model. Within this reduced register, we construct a compatible variational circuit and a Gray-code-inspired measurement strategy whose number of global settings grows only logarithmically with system size. To quantify the overall hardware load, we introduce a volumetric efficiency metric that combines the number of qubits, circuit depth, and the number of measurement settings into a single measure, expressing the overall computation costs. Using this metric, we show that the total space--time sampling volume required in a variational loop can be reduced dramatically from N2 to (logN)3 for a hardware-efficient ansatz, allowing an exponential reduction in time and size of the quantum hardware. These results demonstrate that large, structured solid-state Hamiltonians can be simulated on substantially smaller quantum registers with controlled sampling overhead and manageable circuit complexity, extending the reach of variational quantum algorithms on near-term devices.
  • 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.