PIRSA:22050030 Conference Condensed Matter

DOI10.48660/22050030

Conference/School: Quantum Criticality: Gauge Fields and Matter

Ostmeyer, J. (2022). Reducing the Sign Problem with Complex Neural Networks. Perimeter Institute. https://pirsa.org/22050030

Ostmeyer, Johann. Reducing the Sign Problem with Complex Neural Networks. Perimeter Institute, May. 16, 2022, https://pirsa.org/22050030 

@misc{ pirsa_PIRSA:22050030,
  doi = {10.48660/22050030},
  url = {https://pirsa.org/22050030},
  author = {Ostmeyer, Johann},
  keywords = {Condensed Matter},
  language = {en},
  title = {Reducing the Sign Problem with Complex Neural Networks},
  publisher = {Perimeter Institute},
  year = {2022},
  month = {may},
  note = {PIRSA:22050030 see, \url{https://pirsa.org}}
}

Abstract

The sign problem is arguably the greatest weakness of the otherwise highly efficient, non-perturbative Monte Carlo simulations. Recently, considerable progress has been made in alleviating the sign problem by deforming the integration contour of the path integral into the complex plane and applying machine learning to find near-optimal alternative contours. This deformation however requires a Jacobian determinant calculation which has a generic computational cost scaling as volume cubed. In this talk I am going to present a new architecture with linear runtime, based on complex-valued affine coupling layers.

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