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Found 7 papers, 1 papers with code
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models
We seek a computationally efficient model for a collection of time series arising from multiple interacting entities (a. k. a.
A supplemental investigation of non-linearity in quantum generative models with respect to simulatability and optimization
As a follow-up to this work, we investigate two questions of relevance to the quantum algorithms and machine learning communities: Does introducing this form of non-linearity make the learning model classically simulatable due to the deferred measurement principle?
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.
Introducing Non-Linear Activations into Quantum Generative Models
In this paper, we explore the effect of these dynamics in quantum generative modeling by introducing a model that adds non-linear activations via a neural network structure onto the standard Born Machine framework - the Quantum Neuron Born Machine (QNBM).
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.
Long-time simulations with high fidelity on quantum hardware
Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0. 9 for over 600 time steps.
Quantum Unsupervised and Supervised Learning on Superconducting Processors
Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility.
