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Quantum simulations of stochastic processes offer memory advantages. This study demonstrates a multi-time-step quantum simulation using less memory than classical methods, showcasing quantum interference of future trajectories.

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Area of Science:

  • Quantum information science
  • Computational physics
  • Complex systems modeling

Background:

  • Stochastic process simulations are crucial for understanding complex systems.
  • Quantum computing offers a potential advantage in stochastic simulation by reducing memory requirements.
  • Maintaining quantum coherence is essential for multi-time-step quantum simulations.

Purpose of the Study:

  • To experimentally demonstrate a multi-time-step simulation of a stochastic process using a quantum advantage.
  • To showcase the capability of a photonic quantum information processor in simulating complex systems.
  • To introduce and demonstrate the comparison of classical processes' statistical futures via quantum interference.

Main Methods:

  • Utilized a photonic quantum information processor to execute a multi-time-step simulation.
  • Created quantum superpositions representing all possible future trajectories of the stochastic process.
  • Employed quantum interference to compare the statistical futures of two classical processes.

Main Results:

  • Successfully performed a multi-time-step experimental simulation of a stochastic process with reduced memory footprint compared to classical limits.
  • Demonstrated quantum interference between two 16-dimensional quantum states representing statistical futures.
  • Achieved a high interference visibility of 0.96 ± 0.02.

Conclusions:

  • The experiment validates the potential of quantum devices for efficient stochastic simulations.
  • Quantum interference provides a novel method for comparing classical statistical futures.
  • This work highlights advancements in photonic quantum information processing for complex system modeling.