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Found 6 papers, 1 papers with code
Generative Modeling with Quantum Neurons
Following this result, we showcase the model's ability to learn (express and train on) a wider set of probability distributions, and benchmark the performance against a classical Restricted Boltzmann Machine (RBM).
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.
