A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers

被引:75
作者
Babaeian, Masoud [1 ,2 ]
Nguyen, Dan T. [1 ,3 ]
Demir, Veysi [5 ]
Akbulut, Mehmetcan [1 ]
Blanche, Pierre-A [1 ]
Kaneda, Yushi [1 ]
Guha, Saikat [1 ]
Neifeld, Mark A. [1 ,4 ]
Peyghambarian, N. [1 ]
机构
[1] Univ Arizona, Ctr Opt Sci, Tucson, AZ 85721 USA
[2] Univ Arizona, Dept Phys, Tucson, AZ 85721 USA
[3] Corning Res & Dev Corp, Corning, NY 14831 USA
[4] Univ Arizona, Dept Elect & Comp Engn, Tucson, AZ 85721 USA
[5] ASML Corp, Wilton, CT 06897 USA
关键词
MATRIX-INVERSION; QUANTUM-NOISE; IMPLEMENTATION; OPTIMIZATION; OSCILLATOR; ALGORITHM;
D O I
10.1038/s41467-019-11548-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Combinatorial optimization problems over large and complex systems have many applications in social networks, image processing, artificial intelligence, computational biology and a variety of other areas. Finding the optimized solution for such problems in general are usually in non-deterministic polynomial time (NP)-hard complexity class. Some NP-hard problems can be easily mapped to minimizing an lsing energy function. Here, we present an analog all-optical implementation of a coherent lsing machine (CIM) based on a network of injection-locked multicore fiber (MCF) lasers. The Zeeman terms and the mutual couplings appearing in the Ising Hamiltonians are implemented using spatial light modulators (SLMs). As a proof-of-principle, we demonstrate the use of optics to solve several Ising Hamiltonians for up to thirteen nodes. Overall, the average accuracy of the CIM to find the ground state energy was similar to 90% for 120 trials. The fundamental bottlenecks for the scalability and programmability of the presented CIM are discussed as well.
引用
收藏
页数:11
相关论文
共 70 条
[1]  
[Anonymous], 1996, Approximation algorithms for NP-hard problems
[2]   Information processing using a single dynamical node as complex system [J].
Appeltant, L. ;
Soriano, M. C. ;
Van der Sande, G. ;
Danckaert, J. ;
Massar, S. ;
Dambre, J. ;
Schrauwen, B. ;
Mirasso, C. R. ;
Fischer, I. .
NATURE COMMUNICATIONS, 2011, 2
[3]   Optical Versus Electronic Implementation of Probabilistic Graphical Inference and Experimental Device Demonstration Using Nonlinear Photonics [J].
Babaeian, Masoud ;
Keiffer, Patrick ;
Neifeld, Mark A. ;
Thamvichai, Ratchaneekorn ;
Norwood, Robert A. ;
Blanche, Pierre-A. ;
Wissinger, John ;
Peyghambarian, N. .
IEEE PHOTONICS JOURNAL, 2018, 10 (05)
[4]   Nonlinear optical components for all-optical probabilistic graphical model [J].
Babaeian, Masoud ;
Blanche, Pierre-A. ;
Norwood, Robert A. ;
Kaplas, Tommi ;
Keiffer, Patrick ;
Svirko, Yuri ;
Allen, Taylor G. ;
Chen, Vincent W. ;
Chi, San-Hui ;
Perry, Joseph W. ;
Marder, Seth R. ;
Neifeld, Mark A. ;
Peyghambarian, N. .
NATURE COMMUNICATIONS, 2018, 9
[5]   ON THE COMPUTATIONAL-COMPLEXITY OF ISING SPIN-GLASS MODELS [J].
BARAHONA, F .
JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 1982, 15 (10) :3241-3253
[6]   The Bayesian revolution in genetics [J].
Beaumont, MA ;
Rannala, B .
NATURE REVIEWS GENETICS, 2004, 5 (04) :251-261
[7]  
Blanche PA, 2016, SPRINGER SER MATER S, V240, P1, DOI 10.1007/978-3-319-29334-9_1
[8]  
Blanche PA, 2016, 2016 IEEE PHOTONICS SOCIETY SUMMER TOPICAL MEETING SERIES (SUM), P199, DOI 10.1109/PHOSST.2016.7548802
[9]   Why future supercomputing requires optics [J].
Caulfield, H. John ;
Dolev, Shlomi .
NATURE PHOTONICS, 2010, 4 (05) :261-263
[10]   Optical Data Compression in Time Stretch Imaging [J].
Chen, Claire Lifan ;
Mahjoubfar, Ata ;
Jalali, Bahram .
PLOS ONE, 2015, 10 (04)