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Emergent Equilibrium in All-Optical Single Quantum-Trajectory Ising Machines.

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Summary
This summary is machine-generated.

Multimode optical systems with two-photon processes and nonlocal losses exhibit thermal equilibrium. These systems can function as ultrafast Boltzmann samplers for combinatorial optimization and machine learning applications.

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Area of Science:

  • Quantum optics
  • Nonlinear optics
  • Quantum information science

Background:

  • Investigating complex dynamics in multimode optical systems is crucial for advancing quantum technologies.
  • Understanding the role of quantum noise and nonlocal losses is essential for controlling optical system behavior.

Purpose of the Study:

  • To explore the dynamics of multimode optical systems driven by two-photon processes under nonlocal losses and Gaussian quantum noise.
  • To determine if these systems can achieve thermal equilibrium and function as Boltzmann samplers.
  • To assess their potential for combinatorial optimization and machine learning.

Main Methods:

  • Analysis of quantum trajectories in multimode optical systems.
  • Incorporation of two-photon processes and nonlocal loss mechanisms.
  • Gaussian noise modeling at the quantum level.
  • Investigation of emergent thermal equilibrium governed by an Ising Hamiltonian.

Main Results:

  • Observed emergent thermal equilibrium in a single Gaussian quantum trajectory.
  • Identified an Ising Hamiltonian encoded in the dissipative coupling between modes.
  • Established that the system's effective temperature depends on driving strength relative to the oscillation threshold.
  • Demonstrated the potential for ultrafast Boltzmann sampling due to ultrashort timescales.

Conclusions:

  • Multimode optical systems with specific driving and loss mechanisms can exhibit thermal equilibrium.
  • These systems can operate as ultrafast Boltzmann samplers, leveraging their inherent dynamics.
  • The findings open avenues for efficient hardware realization for combinatorial optimization and machine learning tasks.