# An integrated coupled oscillator network to solve optimization problems

## TL;DR

This paper introduces a silicon chip with 1440 oscillators that efficiently solves optimization problems using a novel coupled oscillator network.

## Contribution

A scalable, silicon-based coupled oscillator network with routable connections for solving optimization problems efficiently.

## Key findings

- The chip solves optimization problems in 950 ns while consuming 319 μW per node.
- Routable connections simplify problem embedding into the oscillator network.
- Calibration ensures robustness against manufacturing variations.

## Abstract

Solving combinatorial optimization problems is essential in scientific, technological, and engineering applications, but can be very time and energy-consuming using classical algorithms executed on digital processors. Oscillator-based Ising machines offer a promising alternative by exploiting the analog coupling between electrical oscillators to solve such optimization problems more efficiently. Here we present the design and the capabilities of our scalable approach to solve Ising and quadratic unconstrained binary optimization problems. This approach includes routable oscillator connections to simplify the time-consuming embedding of the problem into the oscillator network. Our manufactured silicon chip, featuring 1440 oscillators implemented in a 28 nm technology, demonstrates the ability to solve optimization problems in 950 ns while consuming typically 319 μW per node. A frequency, phase, and delay calibration ensures robustness against manufacturing variations. The system is evaluated with multiple sets of benchmark problems to analyze the sensitivity for parameters such as the coupling strength or frequency.

Markus Graber and Klaus Hofmann present a coupled oscillator network, fabricated on a 4.6 mm2 silicon chip with 1440 oscillators and routable connections, designed to solve Ising and other optimization problems efficiently. Their circuit offers a scalable and practical approach for complex optimization problems.