Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Generation of high-resolution handwritten digits with an ion-trap quantum computer
Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …
Optimized quantum compilation for near-term algorithms with openpulse
Quantum computers are traditionally operated by programmers at the granularity of a gate-
based instruction set. However, the actual device-level control of a quantum computer is …
based instruction set. However, the actual device-level control of a quantum computer is …
Anomaly detection with variational quantum generative adversarial networks
Generative adversarial networks (GANs) are a machine learning framework comprising a
generative model for sampling from a target distribution and a discriminative model for …
generative model for sampling from a target distribution and a discriminative model for …
Interpretable quantum advantage in neural sequence learning
Quantum neural networks have been widely studied in recent years, given their potential
practical utility and recent results regarding their ability to efficiently express certain classical …
practical utility and recent results regarding their ability to efficiently express certain classical …
Quantum semantic learning by reverse annealing of an adiabatic quantum computer
Abstract Restricted Boltzmann machines (RBMs) constitute a class of neural networks for
unsupervised learning with applications ranging from pattern classification to quantum state …
unsupervised learning with applications ranging from pattern classification to quantum state …
Classical shadows with symmetries
Classical shadows (CS) have emerged as a powerful way to estimate many properties of
quantum states based on random measurements and classical post-processing. In their …
quantum states based on random measurements and classical post-processing. In their …
Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning
Generative models have the capacity to model and generate new examples from a dataset
and have an increasingly diverse set of applications driven by commercial and academic …
and have an increasingly diverse set of applications driven by commercial and academic …
Comparing the effects of Boltzmann machines as associative memory in generative adversarial networks between classical and quantum samplings
We investigate the quantum effect on machine learning (ML) models exemplified by the
Generative Adversarial Network (GAN), which is a promising deep learning framework. In …
Generative Adversarial Network (GAN), which is a promising deep learning framework. In …
ORQVIZ: visualizing high-dimensional landscapes in variational quantum algorithms
Variational Quantum Algorithms (VQAs) are promising candidates for finding practical
applications of near to mid-term quantum computers. There has been an increasing effort to …
applications of near to mid-term quantum computers. There has been an increasing effort to …