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Supervised learning of the Jaynes-Cummings Hamiltonian.

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Deep neural networks (DNNs) can estimate Jaynes-Cummings Hamiltonian parameters from energy spectra. A combined denoising U-Net and DNN model significantly reduces errors, even with noisy data.

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Area of Science:

  • Quantum mechanics
  • Computational physics
  • Machine learning

Background:

  • Estimating Hamiltonian parameters is crucial for quantum system analysis.
  • Deep neural networks (DNNs) offer potential for complex system modeling.

Purpose of the Study:

  • To assess the effectiveness of DNNs in determining Jaynes-Cummings Hamiltonian parameters using only energy spectra.
  • To evaluate model performance with both clean and noisy spectral data.

Main Methods:

  • Utilized a vanilla DNN (vDNN) for noiseless energy spectra.
  • Investigated the impact of input node count on vDNN accuracy.
  • Employed a denoising U-Net in conjunction with vDNN to handle noisy spectra.

Main Results:

  • vDNN error decreased with more input nodes in noiseless cases.
  • vDNN showed limited resilience to Gaussian noise.
  • The combined U-Net and vDNN model achieved up to a 77% error reduction on noisy data.

Conclusions:

  • DNNs are effective for Hamiltonian parameter estimation from energy spectra.
  • Integrating denoising networks enhances DNN robustness against noise in quantum spectral data.