Rethinking the computer from principles of critical phenomena in neural networks.
We aim to develop special purpose computers, accelerators to modern digital computers, for combinational/continuous optimization problems, quantum simulation of many body systems and deep machine learning. A coherent Ising Machine based on degenerate optical parametric oscillator network solves various combinational (discrete) optimization problems by mapping them onto the Ising model, while a coherent Kuramoto machine based on non-degenerate optical parametric oscillator network solves various continuous optimization problems by mapping them onto the Kuramoto model. A coherent SAT (Satisfiability Problem) solver based on recurrent neural network solves some other optimization problems by mapping them onto the k-satisfiability problem. A coherent mixing solver based on optical homodyning performs larger-scale vector-vector multiplication.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.