Physical Sciences and Mathematics Commons^™

Learning Quantum Operators From Quantum State Pairs, Neil Toronto, Dan A. Ventura

Faculty Publications

Developing quantum algorithms has proven to be very difficult. In this paper, the concept of using classical machine learning techniques to derive quantum operators from examples is presented. A gradient descent algorithm for learning unitary operators from quantum state pairs is developed as a starting point to aid in developing quantum algorithms. The algorithm is used to learn the quantum Fourier transform, an underconstrained two-bit function, and Grover’s iterate.