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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Scaling quantum approximate optimization on near-term hardware.

Phillip C Lotshaw1, Thien Nguyen2,3,4, Anthony Santana2,5

  • 1Quantum Computational Sciences Group, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA. lotshawpc@ornl.gov.

Scientific Reports
|July 20, 2022
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Summary
This summary is machine-generated.

The quantum approximate optimization algorithm (QAOA) requires exponentially more resources with increasing problem size and circuit depth. Enhancements like better hardware connectivity can improve QAOA performance for near-term quantum computing.

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Area of Science:

  • Quantum computing
  • Computational complexity
  • Combinatorial optimization

Background:

  • The quantum approximate optimization algorithm (QAOA) is a leading candidate for demonstrating quantum advantage on near-term devices.
  • Understanding QAOA's resource scaling is crucial for its practical application in solving complex problems.

Purpose of the Study:

  • To quantify the scaling of QAOA resource requirements, specifically the number of measurements needed.
  • To analyze how these requirements are affected by hardware architecture, problem size, and circuit parameters.

Main Methods:

  • Synthesizing optimized QAOA circuits for diverse hardware architectures.
  • Estimating measurement counts for idealized QAOA circuits under noisy gate operations.
  • Analyzing the exponential scaling of resources with problem size, graph degree, and circuit depth.

Main Results:

  • The number of measurements, and thus time-to-solution, scales exponentially with problem size, QAOA depth, and gate infidelities.
  • Resource requirements are also influenced by the degree of the problem graph and the inverse hardware connectivity.
  • Exponential scaling is observed across various hardware architectures.

Conclusions:

  • QAOA's viability on near-term quantum computers is challenged by significant resource scaling issues.
  • Increased hardware connectivity and algorithmic modifications can potentially mitigate these scaling problems.
  • Further research into efficient QAOA implementations is necessary for practical quantum advantage.