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Found 4 papers, 1 papers with code
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs).
Do Quantum Circuit Born Machines Generalize?
To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.
Generalization Metrics for Practical Quantum Advantage in Generative Models
Using the sample-based approach proposed here, any generative model, from state-of-the-art classical generative models such as GANs to quantum models such as Quantum Circuit Born Machines, can be evaluated on the same ground on a concrete well-defined framework.
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability
