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 …
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 …
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 …
Kochen-specker contextuality
A central result in the foundations of quantum mechanics is the Kochen-Specker theorem. In
short, it states that quantum mechanics is in conflict with classical models in which the result …
short, it states that quantum mechanics is in conflict with classical models in which the result …
Generation of high-resolution handwritten digits with an ion-trap quantum computer
Generating high-quality data (eg, images or video) is one of the most exciting and
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …
challenging frontiers in unsupervised machine learning. Utilizing quantum computers in …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Trainability barriers and opportunities in quantum generative modeling
Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …
promise for achieving an advantage using quantum hardware. In this work, we investigate …
Quantum anomaly detection in the latent space of proton collision events at the LHC
The ongoing quest to discover new phenomena at the LHC necessitates the continuous
development of algorithms and technologies. Established approaches like machine …
development of algorithms and technologies. Established approaches like machine …
Generative quantum learning of joint probability distribution functions
Modeling joint probability distributions is an important task in a wide variety of fields. One
popular technique for this employs a family of multivariate distributions with uniform …
popular technique for this employs a family of multivariate distributions with uniform …
Generative quantum machine learning via denoising diffusion probabilistic models
Deep generative models are key-enabling technology to computer vision, text generation,
and large language models. Denoising diffusion probabilistic models (DDPMs) have …
and large language models. Denoising diffusion probabilistic models (DDPMs) have …