In an Ising computer (illustrated here with 4 bits), the variables all converge to a parallel solution. Credit: Journal of Optical Microsystems (2023). DOI: 10.1117/1.JOM.4.1.014501

In our data-driven age, solving complex problems effectively is critical. However, traditional computers often struggle with this task when dealing with a large number of interacting variables, leading to inefficiencies such as the von Neumann constraint. A new type of collective state computing has emerged to solve these optimization problems known as the Ising problem in magnetism.

Here’s how it works: Imagine representing a problem as a graph, where are connected by edges. Each node has two states, either +1 or -1, which represent possible solutions. The objective is to find the configuration that minimizes the total energy of the system based on a concept called Hamiltonian.

Researchers are looking for which can outperform conventional computers to efficiently solve the singling Hamiltonian. A promising approach involves the use of light-based techniques, where information is encoded in properties such as polarization state, phase, or amplitude. These systems can quickly find accurate solutions by taking advantage of effects such as interference and optical feedback.

In one study published I Journal of Optical Microsystems, researchers from the National University of Singapore and the Science, Technology and Research Agency looked at using a system of vertical cavity surface-emitting lasers (VCSELs) to solve this problem. In this setup, information is encoded in the linear polarization states of the VCSELs, with each state corresponding to a possible solution.

The lasers are interconnected, and the interactions between them encode the structure of the problem.

The researchers tested their system on trivial 2-, 3-, and 4-bit Ising problems and found promising results. However, they also identified challenges, such as the need for minimal VCSEL lasing anisotropy, which may be difficult to achieve in practice. Nevertheless, overcoming these challenges could lead to an all-optical VCSEL-based computer architecture capable of solving problems that are currently beyond the reach of conventional computers.

More information:
Brandon Luke et al., Linear Polarization State Encoding for Ising Computing with Optically Injection-Locked VCSELs, Journal of Optical Microsystems (2023). DOI: 10.1117/1.JOM.4.1.014501

Reference: Researchers build computer from array of VCSELs with optical feedback (2024, February 23) Retrieved February 23, 2024 from Obtained

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