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The geometry of quantum learning

  • Author(s): Hunziker, Markus
  • Meyer, David A.
  • Park, Jihun
  • Pommersheim, James
  • Rothstein, Mitch
  • et al.
Abstract

Concept learning provides a natural framework in which to place the problems solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining the tools used in these algorithms—quantum fast transforms and amplitude amplification—with a novel (in this context) tool—a solution method for geometrical optimization problems—we derive a general technique for quantum concept learning. We name this technique “Amplified Impatient Learning” and apply it to construct quantum algorithms solving two new problems: Battleship and Majority, more efficiently than is possible classically.

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