Experimental quantum kernel machine learning with nuclear spins in a solid

  • Takeru Kusumoto
  • ,
  • Kosuke Mitarai
  • ,
  • Keisuke Fujii
  • ,
  • Masahiro Kitagawa
  • ,
  • Makoto Negoro

We employ so-called quantum kernel estimation to exploit complex quantum
dynamics of solid-state nuclear magnetic resonance for machine learning. We
propose to map an input to a feature space by input-dependent Hamiltonian
evolution, and the kernel is estimated by the interference of the evolution.
Simple machine learning tasks, namely one-dimensional regression tasks and
two-dimensional classification tasks, are performed using proton spins which
exhibit correlation over 10 spins. We also performed numerical simulations to
evaluate the performance without the noise inevitable in the actual
experiments. The performance of the trained model tends to increase with the
longer evolution time, or equivalently, with a larger number of spins involved
in the dynamics for certain tasks. This work presents a quantum machine
learning experiment using one of the largest quantum systems to date.

Arxiv Url
Arxiv Url
http://arxiv.org/pdf/1911.12021v1 本文へのリンクあり