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This study introduces a non-variational Quantum Random Access Optimization (QRAO) method using Quantum Alternating Operator Ansatz (QAOA) for optimization problems. Fixed parameters improve scalability for quantum computers.

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Area of Science:

  • Quantum Computing
  • Computational Optimization
  • Algorithm Development

Background:

  • Quantum computers offer potential speedups for complex optimization problems.
  • Current quantum approaches face limitations due to hardware size and error correction overheads.
  • Quantum Random Access Optimization (QRAO) aims to reduce quantum resource requirements.

Purpose of the Study:

  • To develop and benchmark a non-variational QRAO approach for optimization.
  • To overcome the scalability issues of variational QRAO methods.
  • To enable practical QRAO execution on near-term quantum devices.

Main Methods:

  • Implemented a non-variational QRAO using the Quantum Alternating Operator Ansatz (QAOA).
  • Applied the method to the MaxCut optimization problem.
  • Evaluated fixed, instance-independent parameters instead of variational ones.
  • Assessed various QAOA design choices (mixers, initial states, cost operator implementations).

Main Results:

  • Achieved good performance with instance-independent QAOA parameters, eliminating the need for variational training.
  • Identified effective strategies for QAOA design choices in the QRAO context.
  • Demonstrated the viability of a non-variational approach for QRAO.

Conclusions:

  • Non-variational QAOA-based QRAO offers a scalable solution for quantum optimization.
  • This approach removes the bottleneck of instance-specific parameter training.
  • The findings facilitate the practical application of QRAO on early fault-tolerant quantum computers.