Training of quantum circuits on a hybrid quantum computer

TLDR

Generative modeling, a machine‑learning approach used in computer vision and chemical design, is expected to benefit most from the extra resources of near‑term quantum computers. The authors implement a data‑driven quantum‑circuit training algorithm on the Bars‑and‑Stripes dataset using a quantum‑classical hybrid machine. Training is performed by running parameterized circuits on a trapped‑ion quantum computer, feeding the outcomes to a classical optimizer, and applying Particle Swarm and Bayesian optimization strategies. Convergence to the target distribution depends critically on both the quantum hardware and the classical optimization strategy, and the study marks the first successful training of a high‑dimensional universal quantum circuit, underscoring the promise and challenges of hybrid learning schemes.

Abstract

Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.