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A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models

Computer Science

A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models

M. Hibat-allah, M. Mauri, et al.

Discover groundbreaking insights into the comparative performance of quantum and classical generative models in this innovative study by Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla, and Alejandro Perdomo-Ortiz. This research introduces a robust framework to ascertain practical quantum advantage, revealing the efficiency of Quantum Circuit Born Machines in data-limited scenarios—an essential characteristic for real-world applications facing data scarcity.

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Playback language: English
Abstract
This study introduces a framework for quantitatively comparing the generalization performance of quantum and classical generative models to determine practical quantum advantage (PQA). The framework uses a sample-based approach, making it model-agnostic. Quantum Circuit Born Machines (QCBMs) are compared against Transformers, Recurrent Neural Networks, Variational Autoencoders, and Wasserstein Generative Adversarial Networks. Results suggest QCBMs are more efficient in data-limited regimes, a highly desirable feature for real-world applications with scarce data.
Publisher
Communications Physics
Published On
Feb 28, 2024
Authors
Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla, Alejandro Perdomo-Ortiz
Tags
quantum models
classical models
generative models
Quantum Circuit Born Machines
data efficiency
practical quantum advantage
model comparison

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