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Quantum computational advantage with a programmable photonic processor.

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  • 1Xanadu, Toronto, ON, Canada.

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Photonic quantum computers have achieved computational advantage with Borealis, a programmable processor. This breakthrough significantly outperforms classical computers for complex tasks, marking a milestone for quantum computing.

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Area of Science:

  • Quantum Computing
  • Quantum Optics
  • Computational Physics

Background:

  • Quantum computers offer computational advantage over classical systems for specific tasks.
  • Previous photonic quantum processors lacked full gate programmability and were susceptible to spoofing.
  • Demonstrating quantum advantage requires outperforming classical algorithms on well-defined problems.

Purpose of the Study:

  • To demonstrate quantum computational advantage using a dynamically programmable photonic processor.
  • To validate the capabilities of the Borealis quantum processor for Gaussian Boson Sampling.
  • To establish photonics as a viable platform for practical quantum computation.

Main Methods:

  • Utilized Borealis, a photonic processor with dynamic programmability across all gates.
  • Executed Gaussian Boson Sampling (GBS) on 216 entangled squeezed modes with 3D connectivity.
  • Employed a time-multiplexed, photon-number-resolving architecture.

Main Results:

  • Achieved quantum computational advantage, with Borealis outperforming classical simulations by over 50 million times in runtime.
  • Generated GBS experiments with up to 219 photons and a mean photon number of 125.
  • Demonstrated a runtime of 36 microseconds for Borealis, compared to an estimated 9,000+ years for classical supercomputers.

Conclusions:

  • Borealis establishes a new benchmark for photonic quantum computing, showcasing dynamic programmability and computational advantage.
  • The results validate key technological aspects of photonics for building practical quantum computers.
  • This work represents a significant step towards realizing the potential of quantum computation.