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 …
Noisy intermediate-scale quantum algorithms
A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …
integer factorization and unstructured database search requires millions of qubits with low …
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 …
Hybrid quantum-classical algorithms and quantum error mitigation
Quantum computers can exploit a Hilbert space whose dimension increases exponentially
with the number of qubits. In experiment, quantum supremacy has recently been achieved …
with the number of qubits. In experiment, quantum supremacy has recently been achieved …
Supervised quantum machine learning models are kernel methods
M Schuld - arxiv preprint arxiv:2101.11020, 2021 - arxiv.org
With near-term quantum devices available and the race for fault-tolerant quantum computers
in full swing, researchers became interested in the question of what happens if we replace a …
in full swing, researchers became interested in the question of what happens if we replace a …
Exploiting symmetry in variational quantum machine learning
Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …
quantum computers. The success of variational quantum learning models crucially depends …
Is quantum advantage the right goal for quantum machine learning?
M Schuld, N Killoran - Prx Quantum, 2022 - APS
Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
computing. This is in fact a curious choice: the machine-learning algorithms of today are …
[HTML][HTML] Quantum machine learning beyond kernel methods
Abstract Machine learning algorithms based on parametrized quantum circuits are prime
candidates for near-term applications on noisy quantum computers. In this direction, various …
candidates for near-term applications on noisy quantum computers. In this direction, various …
Quantum machine learning: from physics to software engineering
Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …
technology and artificial intelligence. This review provides a two-fold overview of several key …