Quantum machine learning

By Jacob D. Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd. Nature, 2016

2,780 citations recorded by the source corpus.

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/15eded04386a8982ccd5627bd1efe70bbf624c02

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology (2019, background): “ February 21, 2019 arXiv:1902.07490v1 [cs.DC] 20 Feb 2019 and Data Processing [32, 34, 13], Oil/Gas [33, 19], Internet of Things [37, 38], High Performance Computing (HPC) [29, 14], Quantum Computing [10, 9], Agro-informatics [32], Chemical-informatics [39, 35, 16, 15, 17] and many more. Its archite”
Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers (2020, background): “a solution much faster for certain classically-intractable problems. This computational model has proven effective in diverse fields such as cryptography [54], chemistry [28, 48] and machine learning [3, 32]. QC hardware has progressed considerably in the last few years. Prototype systems with 5-20 ”
Variational quantum algorithms (2020, methodology): “to use quantum algorithms for machine learning applications [134, 148, 256, 257].”
Option Pricing using Quantum Computers (2019, background): “[13] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549, 195–202 (2017).”
Quantum Simulators: Architectures and Opportunities (2019, background): “[21] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, Quantum machine learning, Nature 549, 195 (2017).”
Absence of Barren Plateaus in Quantum Convolutional Neural Networks (2020, background): “[14] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549, 195–202 (2017).”
Precision Medicine, AI, and the Future of Personalized Health Care (2020, background): “Emerging computational improvements in natural language processing (NLP), pattern identification, efficient search, prediction, and bias-free reasoning will lead to further capabilities in AI that address currently intractable problems.(21,22)”
Bio-inspired computation: Where we stand and what's next (2019, background): “in terms of processing throughput for many other fields, including ML and large-scale optimization [451,452].”
Connecting ansatz expressibility to gradient magnitudes and barren plateaus (2021, background): “[50] Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549, 195–202 (2017).”
Transfer learning in hybrid classical-quantum neural networks (2019, background): “nal layers for a particular task and dataset of interest (see Fig.1). The aim of this work is to investigate the potential of the transfer learning paradigm in the context of quantum machine learning [10,15,40]. It is not in Andrea Mari:andreamari84@gmail.com Generic dataset Generic network Generic ”