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We developed a scalable architecture for solving complex binary optimization problems using neutral-atom quantum computers. This approach encodes problems into smaller, manageable modules for efficient computation.

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Area of Science:

  • Quantum Computing
  • Optimization Algorithms
  • Computational Physics

Background:

  • Higher-order constrained binary optimization (HCBO) problems are computationally challenging.
  • Current quantum hardware, specifically neutral-atom platforms in the Rydberg blockade regime, offers potential for solving complex optimization tasks.

Purpose of the Study:

  • To present a scalable architecture for solving HCBO problems on neutral-atom quantum hardware.
  • To adapt existing encoding methods for efficient implementation on current quantum devices.

Main Methods:

  • Formulating HCBO problems using parity encoding.
  • Translating these encoded problems into maximum-weight independent set (MWIS) problems on disk graphs.
  • Designing a modular architecture using small MWIS components for problem-independent scalability.

Main Results:

  • Demonstrated a method to directly encode HCBO problems onto neutral-atom hardware.
  • Developed a scalable architecture by composing small, reusable MWIS modules.
  • The proposed architecture is suitable for current neutral-atom quantum processors.

Conclusions:

  • The presented architecture offers a practical and scalable solution for tackling HCBO problems on neutral-atom quantum computers.
  • This work bridges the gap between theoretical HCBO formulations and their implementation on existing quantum hardware.
  • The modular approach is key to achieving scalability for complex optimization tasks.