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] The variational quantum eigensolver: a review of methods and best practices
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …
received significant attention from the research community in recent years. It uses the …
Challenges and opportunities in quantum machine learning
At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …
has the potential of accelerating data analysis, especially for quantum data, with …
The power of quantum neural networks
It is unknown whether near-term quantum computers are advantageous for machine
learning tasks. In this work we address this question by trying to understand how powerful …
learning tasks. In this work we address this question by trying to understand how powerful …
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 …
Noise-induced barren plateaus in variational quantum algorithms
Abstract Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …
Quantum variational algorithms are swamped with traps
One of the most important properties of classical neural networks is how surprisingly
trainable they are, though their training algorithms typically rely on optimizing complicated …
trainable they are, though their training algorithms typically rely on optimizing complicated …
A Lie algebraic theory of barren plateaus for deep parameterized quantum circuits
Variational quantum computing schemes train a loss function by sending an initial state
through a parametrized quantum circuit, and measuring the expectation value of some …
through a parametrized quantum circuit, and measuring the expectation value of some …
Absence of barren plateaus in quantum convolutional neural networks
Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …
efficiently analyzing quantum data. But this excitement has been tempered by the existence …