# Robust Implementation of Generative Modeling with Parametrized Quantum Circuits

## TL;DR

This paper evaluates the robustness of generative modeling using parametrized quantum circuits on hardware, comparing different classical optimizers, circuit ansatze, and depths through extensive repeated experiments.

## Contribution

It provides the first comprehensive on-hardware assessment of classical optimizer and circuit ansatz choices for quantum generative models, leveraging Rigetti's cloud platform.

## Key findings

- Robust performance comparison requires multiple training runs due to stochastic optimizer behavior.
- Different circuit ansatze and depths significantly affect model performance.
- On-hardware experiments reveal variability and insights not captured by simulations.

## Abstract

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansatz, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver's stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires that one perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. Here, we leverage on Rigetti's Quantum Cloud Services to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansatze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes data set.