A strategy for quantum algorithm design assisted by machine learning

作者：Bang Jeongho^*; Ryu Junghee; Yoo Seokwon; Pawlowski Marcin; Lee Jinhyoung

来源：New Journal of Physics, 2014, 16(7): 073017.

DOI：10.1088/1367-2630/16/7/073017

• quantum learning
• quantum automatic control
• quantum algorithm

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 a classical main-feedback system. Our method is applicable for designing quantum oracle-based algorithms. We chose, as a case study, an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte Carlo simulations that our simulator can faithfully learn a quantum algorithm for solving the problem for a given oracle. Remarkably, the learning time is proportional to the square root of the total number of parameters, rather than showing the exponential dependence found in the classical machine learning-based method.

• 出版日期2014-7-14