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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Breaking limitation of quantum annealer in solving optimization problems under constraints.

Masayuki Ohzeki1,2,3

  • 1Graduate School of Information Science, Tohoku University, Sendai, Japan. mohzeki@tohoku.ac.jp.

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Researchers propose a new method using the Hubbard-Stratonovich transformation to solve complex optimization problems on quantum annealers. This approach overcomes limitations of the chimera graph, enabling larger problem sizes without embedding.

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Area of Science:

  • Quantum computing and optimization
  • Statistical mechanics applications

Background:

  • Quantum annealing is a powerful optimization technique utilizing quantum fluctuations.
  • Current quantum annealers use artificial spins on a limited chimera graph, restricting problem size due to sparse connectivity.
  • Existing embedding techniques significantly reduce the number of logical spins for complex, fully connected problems.

Purpose of the Study:

  • To develop an alternative method for solving large-scale optimization problems on quantum annealers.
  • To overcome the limitations of sparse connectivity in chimera graphs for fully connected optimization problems.

Main Methods:

  • Application of the Hubbard-Stratonovich transformation (or its variants) from statistical mechanics.
  • Solving fully connected Ising models directly on the chimera graph without the need for embedding.
  • Testing the method on partition problems, linear equation solving, and real-world traffic flow optimization.

Main Results:

  • The proposed method successfully addresses fully connected optimization problems on the chimera graph.
  • It enables solving larger-scale problems than previously possible with embedding techniques.
  • Nontrivial results were obtained for tested optimization problems, including traffic flow in Sendai and Kyoto.

Conclusions:

  • The Hubbard-Stratonovich transformation offers a viable alternative for quantum annealing optimization.
  • This approach enhances the capability of quantum annealers to handle complex, highly connected problems.
  • The method demonstrates practical applicability in solving significant real-world optimization challenges.