Fetching the paper…
Reading the bibliography…
Variational quantum approaches have shown great promise in finding near-optimal solutions to computationally challenging tasks.
H. Uzawa, Iterative methods for concave programming, Studies in linear and nonlinear programming 6
1958
Earlier work this paper cites.
R. T. Rockafellar, Convex Analysis (Princeton University Press, Princeton, NJ, 1970)
1970
Earlier work this paper cites.
G. M. Korpelevich, The extragradient method for finding saddle points and other problems, Matecon 12
1976
Earlier work this paper cites.
I. Y. Zabotin, Subgradient method to find the saddle point of a convex-concave function, Issledovaniya po Prikladnoi Matematike i Informatike 15
1988
Earlier work this paper cites.
M. Kallio and A. Ruszczyński, Perturbation methods for saddle point computation , Tech. Rep. (International Institute for Applied Systems Analysis, Laxenburg, Austria: WP-94-038, 1994)
1994
Earlier work this paper cites.
L. K. Grover, A fast quantum mechanical algorithm for database search, in Proc. of the Twenty-Eighth Annual ACM Symp. on Theory of Computing , STOC ’96 (ACM Press, New York, NY, USA, 1996) p. 212–219
1996
Earlier work this paper cites.
P. W. Shor, Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer, SIAM Journal on Computing 26
1997
Earlier work this paper cites.
M. Kallio and C. H. Rosa, Large-scale convex optimization via saddle point computation, Operations Research 47
1999
Earlier work this paper cites.
D. P. Bertsekas, Nonlinear Programming , 2nd ed. (Athena Scientific, Belmont, MA, 1999)
1999
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization (Cambridge University Press, New York, NY, 2004)
2004
Earlier work this paper cites.
J. Lofberg, Yalmip: A toolbox for modeling and optimization in matlab, in 2004 IEEE Intl. Conf. on Robotics and Automation (2004) pp. 284–289
2004
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103
2009
Earlier work this paper cites.
R. M. Karp, Reducibility among combinatorial problems (Springer, Springer, Boston, MA, 2010)
2010
Earlier work this paper cites.
X. Wang and I. Davidson, Flexible constrained spectral clustering, in Proc. of the 16th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (2010) pp. 563–572
2010
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, A variational eigenvalue solver on a photonic quantum processor, Nature communications 5
2014
Earlier work this paper cites.
A. Lucas, Ising formulations of many NP problems, Frontiers in Physics 2
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
T.-H. Chang, A. Nedić, and A. Scaglione, Distributed constrained optimization by consensus-based primal-dual perturbation method, IEEE Trans. on Automatic Control 59
2014
Earlier work this paper cites.
X. Wang, B. Qian, and I. Davidson, On constrained spectral clustering and its applications, Data Mining and knowledge discovery 28
2014
Earlier work this paper cites.
I. Hen and M. S. Sarandy, Driver hamiltonians for constrained optimization in quantum annealing, Phys. Rev. A 93
2016
Earlier work this paper cites.
I. Hen and F. M. Spedalieri, Quantum annealing for constrained optimization, Phys. Rev. Appl. 5
2016
Earlier work this paper cites.
P. Ronagh, B. Woods, and E. Iranmanesh, Solving constrained quadratic binary problems via quantum adiabatic evolution, Quantum Info. Comput. 16
2016
Earlier work this paper cites.
P. Wang, C. Shen, A. van den Hengel, and P. H. Torr, Large-scale binary quadratic optimization using semidefinite relaxation and applications, IEEE Trans. on Pattern Analysis and Machine Intelligence 39
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016) http://www.deeplearningbook.org
2016
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 46
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature 549
2017
Cited alongside, same era.
S. Hadfield, Z. Wang, E. G. Rieffel, B. O’Gorman, D. Venturelli, and R. Biswas, Quantum approximate optimization with hard and soft constraints, in ACM Intl. Workshop on Post Moore’s Era Supercomputing (New York, NY, 2017) pp. 15–21
Z. Wang, N. C. Rubin, J. M. Dominy, and E. G. Rieffel, X Y XY mixers: Analytical and numerical results for the quantum alternating operator ansatz, Phys. Rev. A 101
2020
Later among the works it cites.
R. Sweke, F. Wilde, J. Meyer, M. Schuld, P. K. Fährmann, B. Meynard-Piganeau, and J. Eisert, Stochastic gradient descent for hybrid quantum-classical optimization, Quantum 4
2020
Later among the works it cites.
L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. D. Lukin, Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices, Phys. Rev. X 10
2020
Later among the works it cites.
J. M. Kübler, A. Arrasmith, L. Cincio, and P. J. Coles, An adaptive optimizer for measurement-frugal variational algorithms, Quantum 4
2020
Later among the works it cites.
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al. , Variational quantum algorithms, Nature Reviews Physics 3
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
S. Karimi and P. Ronagh, A subgradient approach for constrained binary optimization via quantum adiabatic evolution, Quantum Info. Process. 16
2017
Cited alongside, same era.
C. Ciliberto, M. Herbster, A. D. Ialongo, M. Pontil, A. Rocchetto, S. Severini, and L. Wossnig, Quantum machine learning: a classical perspective, Proc. of the Royal Society A: Mathematical, Physical and Engineering Sciences 474
2018
Cited alongside, same era.
J. Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2
2018
Cited alongside, same era.
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Phys. Rev. A 98
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nature communications 9
2018
Cited alongside, same era.
S. Hadfield, Z. Wang, B. O’Gorman, E. G. Rieffel, D. Venturelli, and R. Biswas, From the quantum approximate optimization algorithm to a quantum alternating operator ansatz, Algorithms 12
2019
Cited alongside, same era.
2021
Later among the works it cites.
P. Díez-Valle, D. Porras, and J. J. García-Ripoll, Quantum variational optimization: The role of entanglement and problem hardness, Phys. Rev. A 104
2021
Later among the works it cites.
S. Harwood, C. Gambella, D. Trenev, A. Simonetto, D. Bernal, and D. Greenberg, Formulating and solving routing problems on quantum computers, IEEE Trans. on Quantum Engineering 2
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Schuld and F. Petruccione, Machine learning with quantum computers (Springer, Cham, Switzerland, 2021)
2021
Later among the works it cites.
A. W. Harrow and J. C. Napp, Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms, Phys. Rev. Letters 126
2021
Later among the works it cites.
M. Schuld, R. Sweke, and J. J. Meyer, Effect of data encoding on the expressive power of variational quantum-machine-learning models, Phys. Rev. A 103
2021
Later among the works it cites.
Qiskit: An open-source framework for quantum computing (2021)
2021
Later among the works it cites.
O. Simeone, An introduction to quantum machine learning for engineers, Foundations and Trends in Signal Processing 16
2022
Later among the works it cites.
S. Gupta, V. Kekatos, and M. Jin, Controlling smart inverters using proxies: A chance-constrained dnn-based approach, IEEE Trans. on Smart Grid 13
2022
Later among the works it cites.
D-Wave Quantum Inc., Hybrid Solvers for Quadratic Optimization , Tech. Rep. (2022)
2022
Later among the works it cites.
2023
Closest in time.
S. Gupta and V. Kekatos, A quantum approach for stochastic constrained binary optimization, in Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Process. (Rhodes, Greece, 2023)
2023
Closest in time.
Gurobi Optimization, LLC, Gurobi Optimizer Reference Manual (2023)
2023
Closest in time.
2024
Closest in time.