Gaussian Boson Sampling with Pseudo-Photon-Number-Resolving Detectors and Quantum Computational Advantage

Area of Science:

• Quantum Information Science
• Quantum Computing
• Quantum Optics

Background:

• Gaussian boson sampling is a key task for demonstrating quantum computational advantage.
• Previous experiments faced limitations in photon detection and noise characterization.
• Scaling quantum systems is crucial for realizing practical quantum computation.

Purpose of the Study:

• To report new Gaussian boson sampling experiments utilizing pseudo-photon-number-resolving detection.
• To develop a more comprehensive model for characterizing noisy Gaussian boson sampling, accounting for partial photon distinguishability.
• To validate quantum-generated samples against classical spoofing methods in the quantum advantage regime.

Main Methods:

• Implementation of Gaussian boson sampling experiments with up to 255 photon-click events.
• Development of a noise characterization model incorporating partial photon distinguishability.
• Application of Bayesian tests and correlation function analysis for sample validation.

Main Results:

• Successful registration of up to 255 photon-click events in Gaussian boson sampling experiments.
• Demonstration of quantum computational advantage, with the Jiǔzhāng 3.0 quantum computer generating samples in microseconds.
• Classical simulation estimates indicate that generating similar samples on a supercomputer like Frontier would require thousands to billions of years.

Conclusions:

• The reported experiments represent a significant advancement in quantum boson sampling capabilities.
• The developed model provides a more accurate characterization of noisy quantum computations.
• The results strongly support the potential of quantum computers to outperform classical systems for specific computational tasks.