Fetching the paper…
Reading the bibliography…
Binary Neural Networks are a promising technique for implementing efficient deep models with reduced storage and computational requirements.
D. Gottesman and I. Chuang, “Quantum digital signatures,” 2001. [Online]. Available: https://arxiv.org/abs/quant-ph/0105032
2001
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, “Quantum algorithm for linear systems of equations,” Phys. Rev. Lett. , vol. 103, p. 150502, 2009. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.103.150502
2009
Earlier work this paper cites.
M. W. Johnson, M. H. S. Amin, S. Gildert et al. , “Quantum annealing with manufactured spins,” Nature , vol. 473, no. 7346, pp. 194–198, 2011. [Online]. Available: https://doi.org/10.1038/nature10012
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
R. Rubinfeld, “Taming big probability distributions,” XRDS , vol. 19, no. 1, p. 24–28, 2012. [Online]. Available: https://doi.org/10.1145/2331042.2331052
2012
Earlier work this paper cites.
Y. Cao, A. Daskin, S. Frankel, and S. Kais, “Quantum circuit design for solving linear systems of equations,” Mol. Phys. , vol. 110, no. 15-16, pp. 1675–1680, 2012. [Online]. Available: https://doi.org/10.1080/00268976.2012.668289
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Pan, Y. Cao, X. Yao et al. , “Experimental realization of quantum algorithm for solving linear systems of equations,” Phys. Rev. A , vol. 89, p. 022313, 2014. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.89.022313
2014
Earlier work this paper cites.
S. Aaronson, “Read the fine print,” Nat. Physics , vol. 11, no. 4, pp. 291–293, 2015. [Online]. Available: https://doi.org/10.1038/nphys3272
2015
Earlier work this paper cites.
S. Boixo, V. N. Smelyanskiy, A. Shabani et al. , “Computational multiqubit tunnelling in programmable quantum annealers,” Nat. Commun. , vol. 7, p. 10327, 2016. [Online]. Available: https://www.doi.org/10.1038/ncomms10327
2016
Cited alongside, same era.
P. L. McMahon, A. Marandi, Y. Haribara et al. , “A fully programmable 100-spin coherent Ising machine with all-to-all connections,” Science , vol. 354, no. 6312, pp. 614–617, 2016. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.aah5178
2016
Cited alongside, same era.
G. Valiant and P. Valiant, “An automatic inequality prover and instance optimal identity testing,” SIAM J. Comput. , vol. 46, no. 1, pp. 429–455, 2017. [Online]. Available: https://doi.org/10.1137/151002526
2017
Cited alongside, same era.
J. Preskill, “Quantum Computing in the NISQ era and beyond,” Quantum , vol. 2, p. 79, 2018. [Online]. Available: https://doi.org/10.22331/q-2018-08-06-79
2018
Cited alongside, same era.
D. V. Fastovets, Y. I. Bogdanov, B. I. Bantysh, and V. F. Lukichev, “Machine learning methods in quantum computing theory,” in Int. Conf. Micro- and Nano-Electronics 2018 , V. F. Lukichev and K. V. Rudenko, Eds., vol. 11022, Int. Soc. Opt. Photon. SPIE, 2019, pp. 752 – 761. [Online]. Available: https://doi.org/10.1117/12.2522427
2019
Later among the works it cites.
D. Pierangeli, G. Marcucci, and C. Conti, “Large-scale photonic ising machine by spatial light modulation,” Phys. Rev. Lett. , vol. 122, p. 213902, 2019. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.122.213902
2019
Later among the works it cites.
A. Kehoe, P. Wittek, Y. Xue, and A. Pozas-Kerstjens, “Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines,” Mach. Learn.: Sci. Technol. , vol. 2, no. 4, p. 045006, 2021. [Online]. Available: https://doi.org/10.1088/2632-2153/abf834
2021
Later among the works it cites.
The Qiskit Team, “Single qubit gates,” Data 100 at UC Berkeley, 2021. [Online]. Available: https://qiskit.org/textbook/ch-states/single-qubit-gates.html
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Lee, J. Sohl-Dickstein, J. Pennington et al. , “Deep neural networks as gaussian processes,” in Int. Conf. on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=B1EA-M-0Z
2018
Cited alongside, same era.
A. G. de G. Matthews, J. Hron, M. Rowland et al. , “Gaussian process behaviour in wide deep neural networks,” in Int. Conf. on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=H1-nGgWC-
2018
Cited alongside, same era.
Y. Takeuchi and T. Morimae, “Verification of many-qubit states,” Phys. Rev. X , vol. 8, no. 2, 2018. [Online]. Available: https://doi.org/10.1103%2Fphysrevx.8.021060
2018
Cited alongside, same era.
Z. Zhao, A. Pozas-Kerstjens, P. Rebentrost, and P. Wittek, “Bayesian deep learning on a quantum computer,” Quantum Mach. Intell. , vol. 1, pp. 41–51, 2019. [Online]. Available: https://doi.org/10.1007/s42484-019-00004-7
2019
Cited alongside, same era.
“The MNIST handritten digit dataset,” http://yann.lecun.com/exdb/mnist/
Cited in the paper.
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Date, D. Arthur, and L. Pusey-Nazzaro, “QUBO formulations for training machine learning models,” Sci. Rep. , vol. 11, p. 10029, 2021. [Online]. Available: https://doi.org/10.1038/s41598-021-89461-4
2021
Later among the works it cites.
M. Sasdelli and T.-J. Chin, “Quantum annealing formulation for binary neural networks,” in 2021 Digital Image Computing: Techniques and Applications (DICTA) , 2021, pp. 1–10. [Online]. Available: https://doi.org/10.1109/DICTA52665.2021.9647321
2021
Later among the works it cites.
H. Hendy and C. Merkel, “Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware,” J. Electron. Imaging , vol. 31, no. 1, p. 010901, 2022. [Online]. Available: https://doi.org/10.1117/1.JEI.31.1.010901
2022
Closest in time.