Seminarium Quantum Technologies — algorytmy kwantowe w erze NISQ

Abstrakt

Recent advancements in quantum hardware have brought quantum computing closer to practical realization, marking the onset of the Noisy Intermediate-Scale Quantum (NISQ) era. In fact, the current quantum processors face significant limitations, including a restricted number of physical qubits and the presence of noise, which degrades the reliability of deep quantum circuits. In this context, Variational Quantum Algorithms (VQAs) have emerged as a promising strategy for harnessing NISQ devices. Byutilizing a quantum computer trained by a classical optimizer, VQAs reduce quantum resource requirements—such as qubit count and circuit depth—compared to algorithms designed for fault-tolerant quantum computers. Despite their potential, VQAs face critical challenges, such as vanishing gradients (barren plateaus), noise-induced errors, and high computational cost for gradient-based optimization, which hinder their scalability. During the seminar, I will explore how Computational Intelligence techniques can address these issues, enhancing both the trainability and efficiency of VQAs. Specifically, I will discuss how Evolutionary Algorithms offer a powerful strategy to overcome trainability limitations in both quantum classification ¹ and optimization ². Additionally, I will show how fuzzy clustering techniques can effectively reduce the complexity of VQAs in quantum optimization ³.

_{¹ G. Acampora, A. Chiatto, and A. Vitiello,Pattern Recognition Letters, Elsevier, 170, 32 (2023).
² G. Acampora, A. Chiatto, and A. Vitiello, Applied Soft Computing, Elsevier, 142, 110296 (2023).
³ G. Acampora, A. Chiatto, and A. Vitiello, 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1 (2023).}

Na seminarium zapraszamy do s. 111 w Gmachu Fizyki, transmisję będzie można obejrzeć na platformie MS Teams.