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Language models for quantum simulation.

Roger G Melko1,2, Juan Carrasquilla3,4,5

  • 1Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada. rmelko@perimeterinstitute.ca.

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Language models are revolutionizing quantum computing by learning complex qubit correlations. These machine learning tools are crucial for simulating quantum devices and achieving quantum advantage.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Simulating quantum computing devices presents a significant challenge due to complex qubit correlations.
  • Machine learning, particularly language models, shows promise in understanding and encoding these quantum states.

Purpose of the Study:

  • To highlight the current contributions of language models in quantum computing.
  • To discuss the future role of language models in achieving quantum advantage.

Main Methods:

  • Utilizing language models adapted from machine learning.
  • Applying these models to learn and encode quantum states and qubit correlations.

Main Results:

  • Language models demonstrate unique abilities in learning complex quantum states.
  • These models are becoming key tools in the simulation of quantum devices.

Conclusions:

  • Language models are essential for overcoming simulation challenges in quantum computing.
  • Their continued development is vital for the pursuit of quantum advantage.