42 research outputs found
Strategy for quantum algorithm design assisted by machine learning
We propose a method for quantum algorithm design assisted by machine
learning. The method uses a quantum-classical hybrid simulator, where a
"quantum student" is being taught by a "classical teacher." In other words, in
our method, the learning system is supposed to evolve into a quantum algorithm
for a given problem assisted by classical main-feedback system. Our method is
applicable to design quantum oracle-based algorithm. As a case study, we chose
an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using
Monte-Carlo simulations that our simulator can faithfully learn quantum
algorithm to solve the problem for given oracle. Remarkably, learning time is
proportional to the square root of the total number of parameters instead of
the exponential dependance found in the classical machine learning based
method.Comment: published versio