Quantum assisted Gaussian process regression

Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.

• Publication year

  2015

• Venue

  Physical Review A

• Publication date

  2015-12-12

• Fields of study

  Mathematics, Physics, Computer Science

• Identifiers
  DOI 10.1103/PhysRevA.99.052331arXiv 1512.03929
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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