Fetching the paper…
Reading the bibliography…
Quantum machine learning and optimization are exciting new areas that have been brought forward by the breakthrough quantum algorithm of Harrow, Hassidim and Lloyd for solving systems of linear equations.
R. Mathias, “The spectral norm of a nonnegative matrix,” Linear Algebra and its Applications , vol. 139, pp. 269–284, 1990
1990
Earlier work this paper cites.
A. Y. Kitaev, “Quantum measurements and the abelian stabilizer problem,” arXiv preprint quant-ph/9511026 , 1995
1995
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, “Introduction to reinforcement learning,” 1998
1998
Earlier work this paper cites.
G. Brassard, P. Hoyer, M. Mosca, and A. Tapp, “Quantum amplitude amplification and estimation,” Contemporary Mathematics , vol. 305, pp. 53–74, 2002
2002
Earlier work this paper cites.
A. Y. Kitaev, A. Shen, and M. N. Vyalyi, Classical and quantum computation . American Mathematical Society Providence, 2002, vol. 47
2002
Earlier work this paper cites.
M. Szegedy, “Quantum speed-up of Markov chain based algorithms,” in Foundations of Computer Science, 2004. Proceedings. 45th Annual IEEE Symposium on . IEEE, 2004, pp. 32–41
2004
Earlier work this paper cites.
D. Bulger, “Quantum basin hopping with gradient-based local optimisation,” arXiv preprint quant-ph/0507193 , 2005
2005
Earlier work this paper cites.
S. P. Jordan, “Fast quantum algorithm for numerical gradient estimation,” Physical review letters , vol. 95, no. 5, p. 050501, 2005
2005
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, “Architectures for a quantum random access memory,” Physical Review A , vol. 78, no. 5, p. 052310, 2008
2008
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, “Quantum algorithm for linear systems of equations,” Physical review letters , vol. 103, no. 15, p. 150502, 2009
2009
Earlier work this paper cites.
S. P. Jordan, “Permutational quantum computing,” arXiv preprint arXiv:0906.2508 , 2009
2009
Earlier work this paper cites.
A. M. Childs, “On the relationship between continuous-and discrete-time quantum walk,” Communications in Mathematical Physics , vol. 294, no. 2, pp. 581–603, 2010
2010
Earlier work this paper cites.
A. Ambainis, “Variable time amplitude amplification and quantum algorithms for linear algebra problems,” in STACS’12 (29th Symposium on Theoretical Aspects of Computer Science) , vol. 14. LIPIcs, 2012, pp. 636–647
2012
Cited alongside, same era.
N. Wiebe, D. Braun, and S. Lloyd, “Quantum algorithm for data fitting,” Physical review letters , vol. 109, no. 5, p. 050505, 2012
2012
Cited alongside, same era.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum algorithms for supervised and unsupervised machine learning,” Arxiv preprint:1307.0411 , 2013
2013
Cited alongside, same era.
A. Ta-Shma, “Inverting well conditioned matrices in quantum logspace,” in Proceedings of the forty-fifth annual ACM symposium on Theory of computing . ACM, 2013, pp. 881–890
2013
Cited alongside, same era.
I. Kerenidis and A. Prakash, “Quantum recommendation systems,” in Proceedings of the 7th conference on Innovations in Theoretical Computer Science (ITCS) , 2017
2017
Closest in time.
Y. Liu and S. Zhang, “Fast quantum algorithms for least squares regression and statistical leverage scores,” Theoretical Computer Science , vol. 657, Part A, pp. 38–47, 2017
2017
Closest in time.
L. Bottou, F. E. Curtis, and J. Nocedal, “Optimization methods for large-scale machine learning,” Siam Review , vol. 60, no. 2, pp. 223–311, 2018
2018
Closest in time.
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,” Nature Physics , vol. 10, no. 9, pp. 631–633, 2014
2014
Cited alongside, same era.
P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Physical review letters , vol. 113, no. 13, p. 130503, 2014
2014
Cited alongside, same era.
S. Aaronson, “Read the fine print,” Nature Physics , vol. 11, no. 4, pp. 291–293, 2015
2015
Cited alongside, same era.
S. Arunachalam, V. Gheorghiu, T. Jochym-O’Connor, M. Mosca, and P. V. Srinivasan, “On the robustness of bucket brigade quantum ram,” New Journal of Physics , vol. 17, no. 12, p. 123010, 2015
2015
Cited alongside, same era.
D. W. Berry, A. M. Childs, A. Ostrander, and G. Wang, “Quantum algorithm for linear differential equations with exponentially improved dependence on precision,” Communications in Mathematical Physics , vol. 356, no. 3, pp. 1057–1081, 2017
2017
Cited alongside, same era.
A. M. Childs, R. Kothari, and R. D. Somma, “Quantum algorithm for systems of linear equations with exponentially improved dependence on precision,” SIAM Journal on Computing , vol. 46, no. 6, pp. 1920–1950, 2017
2017
Cited alongside, same era.
2018
Closest in time.
S. Chakraborty, A. Gilyén, and S. Jeffery, “The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation,” Proceedings of 46th International Colloquium on Automata, Languages and Programming (ICALP) , 2019
2019
Closest in time.
A. Gilyén, S. Arunachalam, and N. Wiebe, “Optimizing quantum optimization algorithms via faster quantum gradient computation,” in Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms . Society for Industrial and Applied Mathematics, 2019, pp. 1425–1444
2019
Closest in time.
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 . ACM, 2019, pp. 193–204
2019
Closest in time.
I. Kerenidis, J. Landman, A. Luongo, and A. Prakash, “q-means: A quantum algorithm for unsupervised machine learning,” Proceedings of Neural Information Processing Systems (NeurIPS) , 2019
2019
Closest in time.
P. Rebentrost, M. Schuld, L. Wossnig, F. Petruccione, and S. Lloyd, “Quantum gradient descent and Newton’s method for constrained polynomial optimization,” New Journal of Physics , vol. 21, no. 7, p. 073023, 2019
2019
Closest in time.
E. Tang, “A quantum-inspired classical algorithm for recommendation systems,” in Proceedings of the fifty-first annual ACM symposium on Theory of computing , 2019
2019
Closest in time.