Fetching the paper…
Reading the bibliography…
Quantum algorithms manipulate the amplitudes of quantum states to find solutions to computational problems.
A. N. Kolmogorov, On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition, in Doklady Akademii Nauk , Vol. 114 (Russian Academy of Sciences, 1957) pp. 953–956
1957
Earlier work this paper cites.
W. F. Ames, Nonlinear partial differential equations in engineering (Academic press, 1965)
1965
Earlier work this paper cites.
D. J. Leeming, The real zeros of the Bernoulli polynomials , Tech. Rep. (1987)
1987
Earlier work this paper cites.
L. K. Grover, Synthesis of quantum superpositions by quantum computation, Physical review letters 85
2000
Earlier work this paper cites.
L. Grover and T. Rudolph, Creating superpositions that correspond to efficiently integrable probability distributions, arXiv preprint quant-ph/0208112 (2002)
2002
Earlier work this paper cites.
B. A. Finlayson, Nonlinear analysis in chemical engineering (Bruce Alan Finlayson, 2003)
2003
Earlier work this paper cites.
L. K. Grover, A different kind of quantum search, arXiv preprint quant-ph/0503205 (2005)
2005
Earlier work this paper cites.
P. A. Forsyth and G. Labahn, Numerical methods for controlled hamilton-jacobi-bellman pdes in finance, Journal of Computational Finance 11
2007
Earlier work this paper cites.
2008
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Physical review letters 103
2009
Earlier work this paper cites.
M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (2010)
2010
Earlier work this paper cites.
S. Burer and A. N. Letchford, Non-convex mixed-integer nonlinear programming: A survey, Surveys in Operations Research and Management Science 17
2012
Earlier work this paper cites.
M. Saeedi and M. Pedram, Linear-depth quantum circuits for n-qubit toffoli gates with no ancilla, Physical Review A 87
2013
Earlier work this paper cites.
L. Deng, A tutorial survey of architectures, algorithms, and applications for deep learning, APSIPA transactions on Signal and Information Processing 3
2014
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, Quantum principal component analysis, Nature Physics 10
2014
Earlier work this paper cites.
A. Montanaro, Quantum speedup of monte carlo methods, Proc. R. Soc. A 471
2015
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT press, 2016)
2016
Earlier work this paper cites.
S. Kimmel, C. Y.-Y. Lin, G. H. Low, M. Ozols, and T. J. Yoder, Hamiltonian simulation with optimal sample complexity, npj Quantum Information 3
2017
Cited alongside, same era.
E. Tang, A quantum-inspired classical algorithm for recommendation systems, Electronic Colloquium on Computational Complexity 128
2018
Cited alongside, same era.
S. Lloyd and C. Weedbrook, Quantum generative adversarial learning, Physical review letters 121
2018
Cited alongside, same era.
2018
Cited alongside, same era.
G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3
2019
Cited alongside, same era.
S. Chakrabarti, R. Krishnakumar, G. Mazzola, N. Stamatopoulos, S. Woerner, and W. J. Zeng, A threshold for quantum advantage in derivative pricing, Quantum 5
2021
Later among the works it cites.
J. Romero and A. Aspuru-Guzik, Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions, Advanced Quantum Technologies 4
2021
Later among the works it cites.
A. G. Rattew, Y. Sun, P. Minssen, and M. Pistoia, The efficient preparation of normal distributions in quantum registers, Quantum 5
2021
Later among the works it cites.
N.-H. Chia, A. P. Gilyén, T. Li, H.-H. Lin, E. Tang, and C. Wang, Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning, Journal of the ACM 69
2022
Later among the works it cites.
A. Gilyén, Z. Song, and E. Tang, An improved quantum-inspired algorithm for linear regression, Quantum 6
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Gilyén, Y. Su, G. H. Low, and N. Wiebe, Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (2019) pp. 193–204
2019
Cited alongside, same era.
K. Mitarai, M. Kitagawa, and K. Fujii, Quantum analog-digital conversion, Physical Review A 99
2019
Cited alongside, same era.
Y. R. Sanders, G. H. Low, A. Scherer, and D. W. Berry, Black-box quantum state preparation without arithmetic, Physical review letters 122
2019
Cited alongside, same era.
C. Zoufal, A. Lucchi, and S. Woerner, Quantum generative adversarial networks for learning and loading random distributions, npj Quantum Information 5
2019
Cited alongside, same era.
A. Holmes and A. Y. Matsuura, Efficient quantum circuits for accurate state preparation of smooth, differentiable functions, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2020) pp. 169–179
2020
Cited alongside, same era.
J. Van Apeldoorn, A. Gilyén, S. Gribling, and R. de Wolf, Quantum sdp-solvers: Better upper and lower bounds, Quantum 4
2020
Cited alongside, same era.
M. Pistoia, S. F. Ahmad, A. Ajagekar, A. Buts, S. Chakrabarti, D. Herman, S. Hu, A. Jena, P. Minssen, P. Niroula, et al. , Quantum machine learning for finance iccad special session paper, in 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) (IEEE, 2021) pp. 1–9
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Bausch, Fast black-box quantum state preparation, Quantum 6
2022
Later among the works it cites.
M. Cerezo, G. Verdon, H.-Y. Huang, L. Cincio, and P. J. Coles, Challenges and opportunities in quantum machine learning, Nature Computational Science 2
2022
Later among the works it cites.
2023
Closest in time.
Z. Holmes, N. J. Coble, A. T. Sornborger, and Y. Subaşı, Nonlinear transformations in quantum computation, Physical Review Research 5
2023
Closest in time.
J. van Apeldoorn, A. Cornelissen, A. Gilyén, and G. Nannicini, Quantum tomography using state-preparation unitaries, in Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) (SIAM, 2023) pp. 1265–1318
2023
Closest in time.
2023
Closest in time.
H. H. S. Chan, R. Meister, T. Jones, D. P. Tew, and S. C. Benjamin, Grid-based methods for chemistry simulations on a quantum computer, Science Advances 9
2023
Closest in time.
2023
Closest in time.