PIRSA:16080019 Conference Condensed Matter

DOI10.48660/16080019

Conference/School: Quantum Machine Learning

Steiger, D. (2016). Racing in parallel: Quantum versus Classical. Perimeter Institute. https://pirsa.org/16080019

Steiger, Damien. Racing in parallel: Quantum versus Classical. Perimeter Institute, Aug. 12, 2016, https://pirsa.org/16080019 

@misc{ pirsa_PIRSA:16080019,
  doi = {10.48660/16080019},
  url = {https://pirsa.org/16080019},
  author = {Steiger, Damien},
  keywords = {Condensed Matter},
  language = {en},
  title = {Racing in parallel: Quantum versus Classical},
  publisher = {Perimeter Institute},
  year = {2016},
  month = {aug},
  note = {PIRSA:16080019 see, \url{https://pirsa.org}}
}

Abstract

In a fair comparison of the performance of a quantum algorithm to a classical one it is important to treat them on equal footing, both regarding resource usage and parallelism. We show how one may otherwise mistakenly attribute speedup due to parallelism as quantum speedup. As an illustration we will go through a few quantum machine learning algorithms, e.g. Quantum Page Rank, and show how a classical parallel computer can solve these problems faster with the same amount of resources. Our classical parallelism considerations are especially important for quantum machine learning algorithms, which either use QRAM, allow for unbounded fanout, or require an all-to-all communication network.

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