Akademik Veri Yönetim Sistemi
International Conference on Technology, Engineering and Science 2023 (IConTES) , Antalya, Türkiye, 16 - 19 Kasım 2023, ss.77, (Özet Bildiri)
Another application of a configuration optimization problem is to calculate the
expected spin configurations of magnetic materials in different shapes and under
different conditions. Quantum Approximate Optimization. Algorithm is a promising
candidate to investigate such material engineering problems using a quantum
computing device. In this work we have considered Ferromagnetic and
Antiferromagnetic materials fabricated as chain with varying sizes. Using Quantum
Approximate Optimization Algorithm we have minimized the hamiltonian of
considered magnetic material and have calculated the most-probable spin alignments.
We have also examined the external magnetic field effect on the spin orientations of
magnetic moments in these materials using aforesaid quantum algorithm. As for the
optimizer of Quantum Approximate Optimization. Algorithm, we have employed a
Quantum Feed Forward Neural Network. We have also investigated the impact of
different hyperparameters of the Quantum Feed Forward Neural Network such as
epoch number or batch size. We observed that Quantum Approximate Optimization
Algorithm is, indeed, a succeeding quantum algorithm to utilize quantum devices to
explore the nature of different magnetic materials with varying sizes
and shapes under different conditions. Moreover, we have seen that Quantum Feed
Forward Neural Network is a legitimate optimizator candidate for Quantum
Approximate Optimization Algorithm for future applications.
Keywords: magnetic materials, quantum approximate optimization