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 …
A high-bias, low-variance introduction to machine learning for physicists
Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …
research and application. The purpose of this review is to provide an introduction to the core …
Artificial intelligence and machine learning for quantum technologies
In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …
of science and technology significantly. In the present perspective article, we explore how …
Quantum circuit optimization with deep reinforcement learning
A central aspect for operating future quantum computers is quantum circuit optimization, ie,
the search for efficient realizations of quantum algorithms given the device capabilities. In …
the search for efficient realizations of quantum algorithms given the device capabilities. In …
Learning high-accuracy error decoding for quantum processors
Building a large-scale quantum computer requires effective strategies to correct errors that
inevitably arise in physical quantum systems. Quantum error-correction codes present a way …
inevitably arise in physical quantum systems. Quantum error-correction codes present a way …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
[HTML][HTML] Optimizing quantum error correction codes with reinforcement learning
Quantum error correction is widely thought to be the key to fault-tolerant quantum
computation. However, determining the most suited encoding for unknown error channels or …
computation. However, determining the most suited encoding for unknown error channels or …
Quantum compiling by deep reinforcement learning
The general problem of quantum compiling is to approximate any unitary transformation that
describes the quantum computation as a sequence of elements selected from a finite base …
describes the quantum computation as a sequence of elements selected from a finite base …
Decoding algorithms for surface codes
Quantum technologies have the potential to solve certain computationally hard problems
with polynomial or super-polynomial speedups when compared to classical methods …
with polynomial or super-polynomial speedups when compared to classical methods …
How to use neural networks to investigate quantum many-body physics
J Carrasquilla, G Torlai - PRX Quantum, 2021 - APS
Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …