Variational quantum algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …
algebra problems are very challenging for classical computers, owing to the extremely high …
[HTML][HTML] Systematic literature review: Quantum machine learning and its applications
D Peral-García, J Cruz-Benito… - Computer Science …, 2024 - Elsevier
Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …
parameterized quantum circuit on a training data set, and subsequently making predictions …
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Quantum computers can be used for supervised learning by treating parametrized quantum
circuits as models that map data inputs to predictions. While a lot of work has been done to …
circuits as models that map data inputs to predictions. While a lot of work has been done to …
Parameterized quantum circuits as machine learning models
Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …
Theory for equivariant quantum neural networks
Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …
face trainability and generalization issues. Inspired by a similar problem, recent …
Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution
It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …
exponential advantage over classical generative adversarial networks. However, quantum …
Experimental quantum generative adversarial networks for image generation
Quantum machine learning is expected to be one of the first practical applications of near-
term quantum devices. Pioneer theoretical works suggest that quantum generative …
term quantum devices. Pioneer theoretical works suggest that quantum generative …
Theoretical guarantees for permutation-equivariant quantum neural networks
Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …
challenges one must overcome before unlocking their full potential. For instance, models …
[BOOK][B] Quantum computing: an applied approach
JD Hidary, JD Hidary - 2019 - Springer
Our world, of course, changed in many other ways as well since the publication of the first
edition. The global pandemic impacted all areas of society and will probably transform how …
edition. The global pandemic impacted all areas of society and will probably transform how …