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Fabrication and Testing of Microfluidic Optomechanical Oscillators
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Published on: May 29, 2014

A poor man's coherent Ising machine based on opto-electronic feedback systems for solving optimization problems.

Fabian Böhm1, Guy Verschaffelt2, Guy Van der Sande3

  • 1Applied Physics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium. fabian.bohm@vub.be.

Nature Communications
|August 10, 2019
PubMed
Summary
This summary is machine-generated.

We introduce a programmable Coherent Ising Machine (CIM) using opto-electronic oscillators. This compact, stable, and cost-effective device solves complex optimization problems, showing performance comparable to existing CIMs.

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Area of Science:

  • Quantum computing
  • Computational physics
  • Optical engineering

Background:

  • Coherent Ising Machines (CIMs) offer potential speed-ups for complex optimization problems by simulating the Ising model.
  • Current CIM implementations face challenges in stability, size, and cost due to complex optical setups.

Purpose of the Study:

  • To propose and test a novel, fully programmable Coherent Ising Machine (CIM).
  • To demonstrate a compact, stable, and cost-effective CIM based on opto-electronic oscillators.

Main Methods:

  • Utilizing opto-electronic oscillators with self-feedback to generate artificial spins.
  • Encoding spins in the intensity of coherent states, avoiding nonlinear optics and large cavities.
  • Testing the device on MAXCUT optimization problems for regular and frustrated graphs with 100 spins.

Main Results:

  • A compact and programmable CIM setup was successfully demonstrated.
  • The proposed CIM achieved performance comparable to or better than existing CIMs.
  • The design offers significant advantages in stability, size, and cost.

Conclusions:

  • The proposed opto-electronic oscillator-based CIM is a viable and advantageous alternative to current designs.
  • This approach simplifies CIM construction and operation, paving the way for practical applications.
  • Programmable CIMs hold promise for efficiently solving computationally hard optimization problems.