Simple Data Gets The Most Out Of Quantum Machine Learning

This paper presents new theoretical research that proves that machine learning on quantum computers requires far less data than previously believed. This finding paves a path to maximizing the usability of today’s noisy, intermediate-scale quantum computers for simulating quantum systems and other tasks better than classical digital computers. The paper builds on previous work by Los Alamos National Laboratory and its collaborators that demonstrated that training a quantum neural network requires only a small amount of data.