Quantum circuit learning

TLDR

The authors propose a classical‑quantum hybrid algorithm, quantum circuit learning, for machine learning on near‑term quantum processors. The framework trains a quantum circuit by iteratively optimizing tunable parameters, enabling low‑depth circuits to learn tasks. Theoretical analysis and simulations demonstrate that the hybrid low‑depth quantum circuit can approximate nonlinear functions, indicating its potential for near‑term quantum machine learning applications.

Abstract

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.