• Corpus ID: 237452490

Improving quantum linear system solvers via a gradient descent perspective

@inproceedings{Gribling2021ImprovingQL,
  title={Improving quantum linear system solvers via a gradient descent perspective},
  author={Sander Gribling and Iordanis Kerenidis and D'aniel Szil'agyi},
  year={2021}
}
Solving systems of linear equations is one of the most important primitives in quantum computing that has the potential to provide a practical quantum advantage in many different areas, including in optimization, simulation, and machine learning. In this work, we revisit quantum linear system solvers from the perspective of convex optimization, and in particular gradient descent-type algorithms. This leads to a considerable constant-factor improvement in the runtime (or, conversely, a several… 

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