[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 …
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 …
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 …
A unified theory of barren plateaus for deep parametrized quantum circuits
AFV CoverSheet Page 1 LA-UR-23-30483 Accepted Manuscript A Lie algebraic theory of
barren plateaus for deep parameterized quantum circuits Cerezo de la Roca, Marco Vinicio …
barren plateaus for deep parameterized quantum circuits Cerezo de la Roca, Marco Vinicio …
Group-invariant quantum machine learning
Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …
exciting. Understanding how QNN properties (for example, the number of parameters M) …
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 …
Diagnosing barren plateaus with tools from quantum optimal control
Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …
to their potential for achieving near-term quantum advantage. However, more work is …
Characterizing barren plateaus in quantum ansätze with the adjoint representation
Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize
parameterized quantum circuits which naturally express Lie groups. It has been postulated …
parameterized quantum circuits which naturally express Lie groups. It has been postulated …