Literature DB >> 28905917

Quantum machine learning.

Jacob Biamonte1,2, Peter Wittek3, Nicola Pancotti4, Patrick Rebentrost5, Nathan Wiebe6, Seth Lloyd7.   

Abstract

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.

Year:  2017        PMID: 28905917     DOI: 10.1038/nature23474

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  32 in total

1.  Quantum optimization for training support vector machines.

Authors:  Davide Anguita; Sandro Ridella; Fabio Rivieccio; Rodolfo Zunino
Journal:  Neural Netw       Date:  2003 Jun-Jul

2.  Differential evolution for many-particle adaptive quantum metrology.

Authors:  Neil B Lovett; Cécile Crosnier; Martí Perarnau-Llobet; Barry C Sanders
Journal:  Phys Rev Lett       Date:  2013-05-28       Impact factor: 9.161

3.  Preconditioned quantum linear system algorithm.

Authors:  B D Clader; B C Jacobs; C R Sprouse
Journal:  Phys Rev Lett       Date:  2013-06-18       Impact factor: 9.161

4.  Experimental realization of a quantum support vector machine.

Authors:  Zhaokai Li; Xiaomei Liu; Nanyang Xu; Jiangfeng Du
Journal:  Phys Rev Lett       Date:  2015-04-08       Impact factor: 9.161

5.  Quantum algorithm for data fitting.

Authors:  Nathan Wiebe; Daniel Braun; Seth Lloyd
Journal:  Phys Rev Lett       Date:  2012-08-02       Impact factor: 9.161

6.  Quantum computing. Defining and detecting quantum speedup.

Authors:  Troels F Rønnow; Zhihui Wang; Joshua Job; Sergio Boixo; Sergei V Isakov; David Wecker; John M Martinis; Daniel A Lidar; Matthias Troyer
Journal:  Science       Date:  2014-06-19       Impact factor: 47.728

7.  High-fidelity spin entanglement using optimal control.

Authors:  Florian Dolde; Ville Bergholm; Ya Wang; Ingmar Jakobi; Boris Naydenov; Sébastien Pezzagna; Jan Meijer; Fedor Jelezko; Philipp Neumann; Thomas Schulte-Herbrüggen; Jacob Biamonte; Jörg Wrachtrup
Journal:  Nat Commun       Date:  2014-02-28       Impact factor: 14.919

8.  Quantum learning without quantum memory.

Authors:  G Sentís; J Calsamiglia; R Muñoz-Tapia; E Bagan
Journal:  Sci Rep       Date:  2012-10-05       Impact factor: 4.379

9.  Quantum Enhanced Inference in Markov Logic Networks.

Authors:  Peter Wittek; Christian Gogolin
Journal:  Sci Rep       Date:  2017-04-19       Impact factor: 4.379

10.  Prediction and real-time compensation of qubit decoherence via machine learning.

Authors:  Sandeep Mavadia; Virginia Frey; Jarrah Sastrawan; Stephen Dona; Michael J Biercuk
Journal:  Nat Commun       Date:  2017-01-16       Impact factor: 14.919

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  68 in total

1.  Solving a Higgs optimization problem with quantum annealing for machine learning.

Authors:  Alex Mott; Joshua Job; Jean-Roch Vlimant; Daniel Lidar; Maria Spiropulu
Journal:  Nature       Date:  2017-10-18       Impact factor: 49.962

2.  Predicting research trends with semantic and neural networks with an application in quantum physics.

Authors:  Mario Krenn; Anton Zeilinger
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-14       Impact factor: 11.205

3.  Keep quantum computing global and open.

Authors:  Jacob D Biamonte; Pavel Dorozhkin; Igor Zacharov
Journal:  Nature       Date:  2019-09       Impact factor: 49.962

Review 4.  Practical quantum advantage in quantum simulation.

Authors:  Andrew J Daley; Immanuel Bloch; Christian Kokail; Stuart Flannigan; Natalie Pearson; Matthias Troyer; Peter Zoller
Journal:  Nature       Date:  2022-07-27       Impact factor: 69.504

Review 5.  Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics.

Authors:  Abicumaran Uthamacumaran
Journal:  Biol Cybern       Date:  2022-06-09       Impact factor: 3.072

6.  Quantum K-means clustering method for detecting heart disease using quantum circuit approach.

Authors:  Narasimha Kaulgud; S S Kavitha
Journal:  Soft comput       Date:  2022-05-31       Impact factor: 3.732

7.  Factoring semi-primes with (quantum) SAT-solvers.

Authors:  Michele Mosca; Sebastian R Verschoor
Journal:  Sci Rep       Date:  2022-05-14       Impact factor: 4.996

8.  High-performance superconducting quantum processors via laser annealing of transmon qubits.

Authors:  Eric J Zhang; Srikanth Srinivasan; Neereja Sundaresan; Daniela F Bogorin; Yves Martin; Jared B Hertzberg; John Timmerwilke; Emily J Pritchett; Jeng-Bang Yau; Cindy Wang; William Landers; Eric P Lewandowski; Adinath Narasgond; Sami Rosenblatt; George A Keefe; Isaac Lauer; Mary Beth Rothwell; Douglas T McClure; Oliver E Dial; Jason S Orcutt; Markus Brink; Jerry M Chow
Journal:  Sci Adv       Date:  2022-05-13       Impact factor: 14.957

Review 9.  Machine learning for molecular and materials science.

Authors:  Keith T Butler; Daniel W Davies; Hugh Cartwright; Olexandr Isayev; Aron Walsh
Journal:  Nature       Date:  2018-07-25       Impact factor: 49.962

10.  Quantum processor-inspired machine learning in the biomedical sciences.

Authors:  Richard Y Li; Sharvari Gujja; Sweta R Bajaj; Omar E Gamel; Nicholas Cilfone; Jeffrey R Gulcher; Daniel A Lidar; Thomas W Chittenden
Journal:  Patterns (N Y)       Date:  2021-04-28
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