Quantum computing for high-energy physics: State of the art and challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …
natural sciences and beyond, with the potential for achieving a so-called quantum …
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 …
Power of data in quantum machine learning
The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …
applications of quantum technologies. However, machine learning tasks where data is …
A rigorous and robust quantum speed-up in supervised machine learning
Recently, several quantum machine learning algorithms have been proposed that may offer
quantum speed-ups over their classical counterparts. Most of these algorithms are either …
quantum speed-ups over their classical counterparts. Most of these algorithms are either …
Is quantum advantage the right goal for quantum machine learning?
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 …
[BOOK][B] Machine learning with quantum computers
M Schuld, F Petruccione - 2021 - Springer
The introduction gives some context about what quantum machine learning is, how it got
established as a sub-discipline of quantum computing and which higher level approaches …
established as a sub-discipline of quantum computing and which higher level approaches …
Information-theoretic bounds on quantum advantage in machine learning
We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …
predicting outcomes of physical experiments. The experiments depend on an input …
[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 …
Synergistic pretraining of parametrized quantum circuits via tensor networks
Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …
day quantum hardware to solve diverse problems in materials science, quantum chemistry …
Shadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising practical
applications for which quantum computers could provide a computational advantage …
applications for which quantum computers could provide a computational advantage …