EVENTI
Nel quadro delle attività del corso Introduction to Quantum Computation (SUPRA) del Dottorato Fisica e Nanoscienze, venerdi' 20/6/2025 in aula F1, alle ore 11:00, il ,Dott. Emanuele Donno, Dip. Ingegneria dell'Innovazione Università del Salento, terrà un seminario dal titolo:
Variational Quantum Algorithms: An Overview
Classical computers encounter fundamental computational barriers when addressing problems such as large-scale quantum system simulation or complex mathematical optimization. These tasks require exponentially scaling computational resources, rendering them intractable even for the most advanced supercomputers.
Variational Quantum Algorithms (VQAs) address these limitations by leveraging current Noisy Intermediate-Scale Quantum (NISQ) devices despite their constraints of limited qubit counts and noise interference. These hybrid methods combine quantum computational capabilities with classical optimization through shallow quantum circuits and integrated error mitigation strategies.
VQAs operate via a quantum-classical partnership utilizing parameterized quantum circuits with optimizable parameters. A classical computer, acting as the orchestrator, iteratively adjusts these parameters to minimize a cost function that quantifies deviation from the target solution, directing the algorithm toward optimal results.
Despite their potential, VQAs face significant challenges in enhancing trainability and computational efficiency. Ongoing research addresses these limitations through advanced parameter initialization techniques, refined measurement protocols, and enhanced optimization algorithms designed to maximize current quantum hardware utilization.
VQAs aim to achieve quantum advantage by solving computational problems more efficiently than classical approaches using existing imperfect quantum hardware. They represent a pragmatic pathway toward near-term quantum computing applications while fault-tolerant quantum systems remain in development, constituting both a technical breakthrough and a paradigmatic shift in computational problem-solving.