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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Related Experiment Video

Updated: Feb 10, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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Boltzmann sampling from the Ising model using quantum heating of coupled nonlinear oscillators.

Hayato Goto1, Zhirong Lin2, Yasunobu Nakamura2,3

  • 1Frontier Research Laboratory, Corporate Research & Development Center, Toshiba Corporation, 1, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki, 212-8582, Japan. hayato1.goto@toshiba.co.jp.

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Dissipative quantum bifurcation machines (QbM) exhibit a Boltzmann-like output distribution, mapping optimization problem costs to energy. This suggests applications in quantum computing and AI-driven Boltzmann sampling.

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Area of Science:

  • Quantum physics
  • Artificial intelligence
  • Computational complexity

Background:

  • A network of Kerr-non-linear parametric oscillators was proposed for combinatorial optimization using quantum adiabatic evolution.
  • The behavior of the quantum bifurcation machine (QbM) in the presence of dissipation was not previously understood.

Purpose of the Study:

  • To investigate the impact of dissipation on the quantum bifurcation machine.
  • To determine if dissipative QbMs can be used for Boltzmann sampling.

Main Methods:

  • Numerical simulations of a dissipative network of Kerr-non-linear parametric oscillators.
  • Generalization of quantum heating concepts to coupled nonlinear oscillators.

Main Results:

  • The output probability distribution of the dissipative QbM was found to be Boltzmann-like.
  • The energy in the Boltzmann distribution corresponds to the cost function of the optimization problem.
  • A theoretical explanation for the Boltzmann distribution was provided by generalizing quantum heating.

Conclusions:

  • Dissipative quantum bifurcation machines can generate Boltzmann-like distributions relevant to optimization problems.
  • Driven dissipative nonlinear oscillator networks show potential for Boltzmann sampling applications.
  • This research bridges quantum computing approaches with machine learning techniques in artificial intelligence.