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Gaussian boson sampling (GBS) on the Jiǔzhāng quantum computer enhances classical graph algorithms. This quantum enhancement persists and shows robustness against noise in noisy intermediate-scale quantum devices.

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Area of Science:

  • Quantum computing
  • Computational complexity
  • Photonic quantum systems

Background:

  • Gaussian boson sampling (GBS) is a quantum computational advantage protocol.
  • GBS has mathematical links to graph problems and quantum chemistry.
  • Classical stochastic algorithms can potentially be enhanced by GBS samples for graph feature searching.

Purpose of the Study:

  • To investigate the enhancement of classical stochastic algorithms using GBS on a noisy quantum device.
  • To determine if GBS enhancement persists and scales with system size in a computationally relevant regime.
  • To assess the robustness of GBS enhancement under noise on current quantum hardware.

Main Methods:

  • Utilizing the Jiǔzhāng noisy intermediate-scale quantum computer.
  • Generating GBS samples from a 144-mode fully connected photonic processor.
  • Achieving up to 80 photon clicks in the quantum computational advantage regime.

Main Results:

  • Experimental observation of GBS enhancement for graph problems.
  • Demonstration of enhancement persistence with a large photon-click number.
  • Evidence of robustness of the GBS enhancement under certain noise conditions.

Conclusions:

  • Gaussian boson sampling provides a demonstrable enhancement for classical graph algorithms on noisy quantum hardware.
  • The observed enhancement is robust and scales in a computationally relevant regime.
  • This work paves the way for testing real-world problems on current quantum computers and inspires new algorithm development.