publication . Preprint . 2017

Machine learning \& artificial intelligence in the quantum domain

Dunjko, Vedran; Briegel, Hans J.;
Open Access English
  • Published: 08 Sep 2017
Abstract
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can ...
Subjects
free text keywords: Quantum Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Download from
285 references, page 1 of 19

P. Wittek. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Insights. Elsevier, AP, 2014a. ISBN 9780128009536. URL https://books.google.de/books?id= PwUongEACAAJ.

Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. The quest for a quantum neural network. Quantum Information Processing, 13(11):2567{2586, Nov 2014a. ISSN 1573-1332. URL http://dx.doi.org/10. 1007/s11128-014-0809-8. [OpenAIRE]

Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. Quantum machine learning, 2016, arXiv:1611.09347.

Srinivasan Arunachalam and Ronald de Wolf. A survey of quantum learning theory. CoRR, abs/1701.06806, 2017. URL http://arxiv.org/abs/1701.06806.

Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig. Quantum machine learning: a classical perspective, 2017, arXiv:1707.08561.

Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press, New York, NY, USA, 10th edition, 2011. ISBN 1107002176, 9781107002173.

Yuri Manin. Computable and Uncomputable. Sovetskoye Radio, 1980.

Richard Feynman. Simulating physics with computers. International Journal of Theoretical Physics, 21 (6-7):467{488, June 1982. ISSN 0020-7748. URL http://dx.doi.org/10.1007/bf02650179. [OpenAIRE]

Peter W. Shor. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5):1484{1509, oct 1997. URL https://doi.org/10.1137/ s0097539795293172.

Andrew M. Childs and Wim van Dam. Quantum algorithms for algebraic problems. Rev. Mod. Phys., 82: 1{52, Jan 2010. URL https://link.aps.org/doi/10.1103/RevModPhys.82.1. [OpenAIRE]

Ashley Montanaro. Quantum algorithms: an overview. npjQI, 2:15023 EP {, Jan 2016. URL http: //dx.doi.org/10.1038/npjqi.2015.23. Review Article.

Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd. Quantum algorithm for linear systems of equations. Phys. Rev. Lett., 103:150502, Oct 2009. URL https://link.aps.org/doi/10.1103/PhysRevLett.103. 150502. [OpenAIRE]

Andrew M. Childs, Robin Kothari, and Rolando D. Somma. Quantum linear systems algorithm with exponentially improved dependence on precision, 2015, arXiv:1511.02306.

Patrick Rebentrost, Adrian Ste ens, and Seth Lloyd. Quantum singular value decomposition of non-sparse low-rank matrices, 2016a, arXiv:1607.05404. [OpenAIRE]

David Poulin and Pawel Wocjan. Sampling from the thermal quantum gibbs state and evaluating partition functions with a quantum computer. Phys. Rev. Lett., 103:220502, Nov 2009. URL https://link.aps. org/doi/10.1103/PhysRevLett.103.220502.

285 references, page 1 of 19
Abstract
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can ...
Subjects
free text keywords: Quantum Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Download from
285 references, page 1 of 19

P. Wittek. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Insights. Elsevier, AP, 2014a. ISBN 9780128009536. URL https://books.google.de/books?id= PwUongEACAAJ.

Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. The quest for a quantum neural network. Quantum Information Processing, 13(11):2567{2586, Nov 2014a. ISSN 1573-1332. URL http://dx.doi.org/10. 1007/s11128-014-0809-8. [OpenAIRE]

Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. Quantum machine learning, 2016, arXiv:1611.09347.

Srinivasan Arunachalam and Ronald de Wolf. A survey of quantum learning theory. CoRR, abs/1701.06806, 2017. URL http://arxiv.org/abs/1701.06806.

Carlo Ciliberto, Mark Herbster, Alessandro Davide Ialongo, Massimiliano Pontil, Andrea Rocchetto, Simone Severini, and Leonard Wossnig. Quantum machine learning: a classical perspective, 2017, arXiv:1707.08561.

Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press, New York, NY, USA, 10th edition, 2011. ISBN 1107002176, 9781107002173.

Yuri Manin. Computable and Uncomputable. Sovetskoye Radio, 1980.

Richard Feynman. Simulating physics with computers. International Journal of Theoretical Physics, 21 (6-7):467{488, June 1982. ISSN 0020-7748. URL http://dx.doi.org/10.1007/bf02650179. [OpenAIRE]

Peter W. Shor. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5):1484{1509, oct 1997. URL https://doi.org/10.1137/ s0097539795293172.

Andrew M. Childs and Wim van Dam. Quantum algorithms for algebraic problems. Rev. Mod. Phys., 82: 1{52, Jan 2010. URL https://link.aps.org/doi/10.1103/RevModPhys.82.1. [OpenAIRE]

Ashley Montanaro. Quantum algorithms: an overview. npjQI, 2:15023 EP {, Jan 2016. URL http: //dx.doi.org/10.1038/npjqi.2015.23. Review Article.

Aram W. Harrow, Avinatan Hassidim, and Seth Lloyd. Quantum algorithm for linear systems of equations. Phys. Rev. Lett., 103:150502, Oct 2009. URL https://link.aps.org/doi/10.1103/PhysRevLett.103. 150502. [OpenAIRE]

Andrew M. Childs, Robin Kothari, and Rolando D. Somma. Quantum linear systems algorithm with exponentially improved dependence on precision, 2015, arXiv:1511.02306.

Patrick Rebentrost, Adrian Ste ens, and Seth Lloyd. Quantum singular value decomposition of non-sparse low-rank matrices, 2016a, arXiv:1607.05404. [OpenAIRE]

David Poulin and Pawel Wocjan. Sampling from the thermal quantum gibbs state and evaluating partition functions with a quantum computer. Phys. Rev. Lett., 103:220502, Nov 2009. URL https://link.aps. org/doi/10.1103/PhysRevLett.103.220502.

285 references, page 1 of 19
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue