Efficient Computation Using Spatial-Photonic Ising Machines: Utilizing Low-Rank and Circulant Matrix Constraints

Autor:	Wang, Richard Zhipeng, Cummins, James S., Syed, Marvin, Stroev, Nikita, Pastras, George, Sakellariou, Jason, Tsintzos, Symeon, Askitopoulos, Alexis, Veraldi, Daniele, Strinati, Marcello Calvanese, Gentilini, Silvia, Pierangeli, Davide, Conti, Claudio, Berloff, Natalia G.
Rok vydání:	2024
Předmět:	Physics - Computational Physics Condensed Matter - Disordered Systems and Neural Networks Computer Science - Emerging Technologies Computer Science - Machine Learning Physics - Optics
Druh dokumentu:	Working Paper
Popis:	We explore the potential of spatial-photonic Ising machines (SPIMs) to address computationally intensive Ising problems that employ low-rank and circulant coupling matrices. Our results indicate that the performance of SPIMs is critically affected by the rank and precision of the coupling matrices. By developing and assessing advanced decomposition techniques, we expand the range of problems SPIMs can solve, overcoming the limitations of traditional Mattis-type matrices. Our approach accommodates a diverse array of coupling matrices, including those with inherently low ranks, applicable to complex NP-complete problems. We explore the practical benefits of low-rank approximation in optimization tasks, particularly in financial optimization, to demonstrate the real-world applications of SPIMs. Finally, we evaluate the computational limitations imposed by SPIM hardware precision and suggest strategies to optimize the performance of these systems within these constraints. Comment: 15 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.01400 Zobrazit plný text záznamu View this record from Arxiv