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 …
NISQ computing: where are we and where do we go?
In this short review article, we aim to provide physicists not working within the quantum
computing community a hopefully easy-to-read introduction to the state of the art in the field …
computing community a hopefully easy-to-read introduction to the state of the art in the field …
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 …
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) …
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 …
Digital quantum simulation of open quantum systems using quantum imaginary–time evolution
Quantum simulation on emerging quantum hardware is a topic of intense interest. While
many studies focus on computing ground-state properties or simulating unitary dynamics of …
many studies focus on computing ground-state properties or simulating unitary dynamics of …
Building spatial symmetries into parameterized quantum circuits for faster training
Practical success of quantum learning models hinges on having a suitable structure for the
parameterized quantum circuit. Such structure is defined both by the types of gates …
parameterized quantum circuit. Such structure is defined both by the types of gates …
Can error mitigation improve trainability of noisy variational quantum algorithms?
Abstract Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-
term quantum advantage. However, recent studies have shown that noise can severely limit …
term quantum advantage. However, recent studies have shown that noise can severely limit …
Improved Hamiltonians for quantum simulations of gauge theories
Quantum simulations of lattice gauge theories for the foreseeable future will be hampered by
limited resources. The historical success of improved lattice actions in classical simulations …
limited resources. The historical success of improved lattice actions in classical simulations …
Primitive quantum gates for an discrete subgroup: Binary tetrahedral
EJ Gustafson, H Lamm, F Lovelace, D Musk - Physical Review D, 2022 - APS
We construct a primitive gate set for the digital quantum simulation of the binary tetrahedral
(BT) group on two quantum architectures. This non-Abelian discrete group serves as a crude …
(BT) group on two quantum architectures. This non-Abelian discrete group serves as a crude …