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Interfering trajectories in experimental quantum-enhanced stochastic simulation

Author

Listed:
  • Farzad Ghafari

    (Griffith University)

  • Nora Tischler

    (Griffith University)

  • Carlo Di Franco

    (Nanyang Technological University
    Nanyang Technological University)

  • Jayne Thompson

    (National University of Singapore)

  • Mile Gu

    (Nanyang Technological University
    Nanyang Technological University
    National University of Singapore)

  • Geoff J. Pryde

    (Griffith University)

Abstract

Simulations of stochastic processes play an important role in the quantitative sciences, enabling the characterisation of complex systems. Recent work has established a quantum advantage in stochastic simulation, leading to quantum devices that execute a simulation using less memory than possible by classical means. To realise this advantage it is essential that the memory register remains coherent, and coherently interacts with the processor, allowing the simulator to operate over many time steps. Here we report a multi-time-step experimental simulation of a stochastic process using less memory than the classical limit. A key feature of the photonic quantum information processor is that it creates a quantum superposition of all possible future trajectories that the system can evolve into. This superposition allows us to introduce, and demonstrate, the idea of comparing statistical futures of two classical processes via quantum interference. We demonstrate interference of two 16-dimensional quantum states, representing statistical futures of our process, with a visibility of 0.96 ± 0.02.

Suggested Citation

  • Farzad Ghafari & Nora Tischler & Carlo Di Franco & Jayne Thompson & Mile Gu & Geoff J. Pryde, 2019. "Interfering trajectories in experimental quantum-enhanced stochastic simulation," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08951-2
    DOI: 10.1038/s41467-019-08951-2
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    Cited by:

    1. Kang-Da Wu & Chengran Yang & Ren-Dong He & Mile Gu & Guo-Yong Xiang & Chuan-Feng Li & Guang-Can Guo & Thomas J. Elliott, 2023. "Implementing quantum dimensionality reduction for non-Markovian stochastic simulation," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

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