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The quest for a Quantum Neural Network
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“has produced noteworthy advances in quantum random walks, 38,39 quantum control, 40 open quantum systems, 41 and pioneering work on quantum neural networks, 42 quantum machine learning, 43 and quantum processes in the brain.44”
From Toward a quantum future for South Africa (2021) · cites this paper as [42]
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“A key challenge to overcome is the clash between the nonlinear, dissipative dynamics of neural network computing and the linear, reversible dynamics of quantum computing.24”
From Quantum generalisation of feedforward neural networks (2017) · cites this paper as [24]
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“We make the individual neurons reversible and then naturally generalise them to being quantum reversible (unitary).This resolves the classical-quantum clash mentioned above from ref. 24.”
From Quantum generalisation of feedforward neural networks (2017) · cites this paper as [24]
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
Theory of open quantum systems
Xu, R., & Yan, Y. (2002) · The Journal of Chemical Physics · doi:10.1063/1.1474579 · Cited by 6,622
Introduction to the theory of neural computation
Hertz, J., Krogh, A., & Palmer, R. G. (1994) · Neural Networks · doi:10.1016/0893-6080(94)90014-0 · Cited by 6,410
The Theory of Open Quantum Systems
Breuer, H.-P., & Petruccione, F. (2007) · doi:10.1093/acprof:oso/9780199213900.001.0001 · Cited by 5,938
Self-citation
Research works printed but not linked: 4
- Tóth, G., Lent, C.S., Tougaw, P.D., Brazhnik, Y., Weng, W., Porod, W., Liu, R.-W., Huang, Y.-F.: Quantum cellular neural networks (preprint). cond-mat/0005038 (2000) · arXiv
- Peruš, M.: Neural networks as a basis for quantum associative networks. Neural Netw. World 10(6), 1001–1013 (2000)
- Faber, J., Giraldi, G.A.: Quantum models for artificial neural networks. Electronically available: http://arquivosweb.lncc.br/pdfs/QNN-Review.pdf (2002)
- Siomau, M.: Quantum perceptron (preprint). arXiv:1210.6626 (2012) · arXiv
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Resummation for Nonequilibrium Perturbation Theory and Application to Open Quantum Lattices
Li, A. C. Y., Petruccione, F., & Koch, J. (2016) · Physical Review X · doi:10.1103/physrevx.6.021037 · Cited by 43
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Gupta, S., Mohanta, S., Chakraborty, M., & Ghosh, S. (2017) · International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) · doi:10.1109/iemecon.2017.8079602 · Cited by 33
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Kocherla, S., Song, Z., Chrit, F. E., Gard, B., Dumitrescu, E., Alexeev, A., & Bryngelson, S. H. (2024) · AVS Quantum Science · doi:10.1116/5.0217675 · Cited by 26
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Schuld, M., & Petruccione, F. (2016) · Springer eBooks · doi:10.1007/978-1-4899-7502-7_913-1 · Cited by 19
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Hybrid classical-quantum machine learning based on dissipative two-qubit channels
Ghasemian, E., & Tavassoly, M. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-24346-8 · Cited by 18
Metadata
- Authors
- Schuld, M., Sinayskiy, I., & Petruccione, F.
- Affiliations
- University of KwaZulu-Natal (ZA)
- National Institute for Theoretical Physics (ZA)
- Corresponding author
- Schuld, Maria
- Venue
- Quantum Information Processing
- Published
- 2014-08-26
- Volume
- 13
- Issue
- 11
- Pages
- 2567-2586
- ISSN
- 1570-0755 (ISSN-L)
- 1573-1332
- OpenAlex
- W1990514347
- Cited by
- 671
- References
- 84
- Citations by year
- 2026: 83 · 2025: 131 · 2024: 72 · 2023: 80 · 2022: 51 · 2021: 83 · 2020: 49 · 2019: 41 · 2018: 38 · 2017: 17 · 2016: 15 · 2015: 6 · 2014: 5
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Quantum Information Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- quantum neural networks, Hopfield neural network, associative memory, quantum computing, artificial neural networks, nonlinear dynamics, unitary dynamics
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