Approximate techniques, including Generative Adversarial Networks (qGANs), were proposed in literature to reduce the depth of data loading circuits.
Approximate techniques, including Generative Adversarial Networks (qGANs), were proposed in literature to reduce the depth of data loading circuits.
Abstract—Loading data efficiently from classical memories to quantum computers is a key challenge in the current era of quantum computing.
Nov 22, 2019 · We use quantum Generative Adversarial Networks (qGANs) to facilitate efficient learning and loading of generic probability distributions - implicitly given by ...
Optimization. Conference Paper. Optimized Quantum Generative Adversarial Networks for Distribution Loading. September 2022. DOI:10.1109/QCE53715.2022.00132.
Tuning a qGAN to balance accuracy and training time is a hard task that becomes paramount when target distributions are multivariate.
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Duration: 10:23
Posted: Nov 28, 2022
Posted: Nov 28, 2022
PyTorch qGAN Implementation#. Overview#. This tutorial introduces step-by-step how to build a PyTorch-based Quantum Generative Adversarial Network algorithm ...
Feb 2, 2024 · The generator of the QC-GAN consists of a quantum variational circuit together with a one-layer neural network, and the discriminator consists ...
Aug 31, 2022 · TL;DR: This work uses quantum Generative Adversarial Networks (qGANs) to facilitate efficient learning and loading of generic probability ...