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Hybrid classical quantum optimization methods have become an important tool for efficiently solving problems in the current generation of NISQ computers.
S. J. Reddi, S. Kale, and S. Kumar, On the convergence of adam and beyond (2019), arXiv:1904.09237
1904
Earlier work this paper cites.
1907
Earlier work this paper cites.
N. Yamamoto, On the natural gradient for variational quantum eigensolver (2019), arXiv:1909.05074
1909
Earlier work this paper cites.
W. Ritz, Über eine neue methode zur lösung gewisser variationsprobleme der mathematischen physik, J. Reine Angew. Math. 135
1909
Earlier work this paper cites.
W. Wirtinger, Zur formalen theorie der funktionen von mehr komplexen veränderlichen, Math. Ann. 97
1927
Earlier work this paper cites.
H. Robbins and S. Monro, A stochastic approximation method, Ann. Math. Stat. 22
1951
Earlier work this paper cites.
J. Kiefer and J. Wolfowitz, Stochastic estimation of the maximum of a regression function, Ann. Math. Stat. 23
1952
Earlier work this paper cites.
M. J. D. Powell, An efficient method for finding the minimum of a function of several variables without calculating derivatives, Comput. J. 7
1964
Earlier work this paper cites.
R. Fletcher and C. M. Reeves, Function minimization by conjugate gradients, Comput. J. 7
1964
Earlier work this paper cites.
J. A. Nelder and R. Mead, A simplex method for function minimization, Comput. J. 7
1965
Earlier work this paper cites.
H. J. Kushner and D. S. Clark, Stochastic Approximation Methods for Constrained and Unconstrained Systems , Vol. 26 (Springer New York, 1978)
1978
Earlier work this paper cites.
S. G. Nash, Newton-type minimization via the lanczos method, SIAM J. Numer. Anal. 21
1984
Earlier work this paper cites.
J. C. Spall, A stochastic approximation technique for generating maximum likelihood parameter estimates, in 1987 American Control Conference (1987) pp. 1161–1167
1987
Earlier work this paper cites.
D. Kraft, A software package for sequential quadratic programming , Deutsche Forschungs- und Versuchsanstalt für Luft- und Raumfahrt Köln: Forschungsbericht (Wiss. Berichtswesen d. DFVLR, 1988)
1988
Earlier work this paper cites.
M. J. D. Powell, A direct search optimization method that models the objective and constraint functions by linear interpolation, in Advances in Optimization and Numerical Analysis (Springer Netherlands, 1994) pp. 51–67
1994
Earlier work this paper cites.
R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A limited memory algorithm for bound constrained optimization, SIAM J. Numer. Anal. 16
1995
Earlier work this paper cites.
H. J. Kushner and G. G. Yin, Stochastic Approximation Algorithms and Applications (Springer New York, 1997)
1997
Earlier work this paper cites.
M. Lalee, J. Nocedal, and T. Plantenga, On the implementation of an algorithm for large-scale equality constrained optimization, SIAM J. Numer. Anal. 8
1998
Earlier work this paper cites.
S. Amari, Natural gradient works efficiently in learning, Neural Comput. 10
1998
Earlier work this paper cites.
D. Saad, ed., On-Line Learning in Neural Networks (Cambridge University Press, 1999)
1999
Earlier work this paper cites.
J. Spall, Adaptive stochastic approximation by the simultaneous perturbation method, IEEE Trans. Autom. Contr. 45
2000
Earlier work this paper cites.
G. Yan and H. Fan, A newton-like algorithm for complex variables with applications in blind equalization, IEEE Trans. Signal Process. 48
2000
Earlier work this paper cites.
S. Lloyd, Coherent quantum feedback, Phys. Rev. A 62
2000
Earlier work this paper cites.
H. Buhrman, R. Cleve, J. Watrous, and R. de Wolf, Quantum fingerprinting, Phys. Rev. Lett. 87
2001
Earlier work this paper cites.
X. Zhu and J. C. Spall, A modified second-order SPSA optimization algorithm for finite samples, Int. J. Adapt. Control Signal Process. 16
2002
Earlier work this paper cites.
S. Amari and S. Douglas, Why natural gradient?, in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) (IEEE, 2002)
2002
Earlier work this paper cites.
S. Chu, Cold atoms and quantum control, Nature 416
2002
Earlier work this paper cites.
A. E. Albert and L. A. Gardner, Stochastic Approximation and NonLinear Regression (The MIT Press, 2003)
2003
Earlier work this paper cites.
N. Khaneja, T. Reiss, C. Kehlet, T. Schulte-Herbrüggen, and S. J. Glaser, Optimal control of coupled spin dynamics: design of NMR pulse sequences by gradient ascent algorithms, J. Magn. Reson. 172
2005
Earlier work this paper cites.
R. van Handel, J. K. Stockton, and H. Mabuchi, Modelling and feedback control design for quantum state preparation, J. Opt. B: Quantum Semiclass. Opt. 7
2005
Earlier work this paper cites.
Z. Wu and H. Yang, Validity of the quantum adiabatic theorem, Phys. Rev. A 72
2005
Earlier work this paper cites.
H. Häffner, W. Hänsel, C. F. Roos, J. Benhelm, D. Chek-al kar, M. Chwalla, T. Körber, U. D. Rapol, M. Riebe, P. O. Schmidt, et al. , Scalable multiparticle entanglement of trapped ions, Nature 438
2005
Earlier work this paper cites.
J. Nocedal and S. Wright, Numerical Optimization (Springer New York, 2006)
2006
Earlier work this paper cites.
J. Spall, Introduction to stochastic search and optimization. estimation, simulation, and control, IEEE Trans. Neural Netw. Learn. Syst. 18
2007
Earlier work this paper cites.
J. Werschnik and E. K. U. Gross, Quantum optimal control theory, J. Phys. B 40
2007
Earlier work this paper cites.
2008
Earlier work this paper cites.
K. Kreutz-Delgado, The complex gradient operator and the cr-calculus (2009), arXiv:0906.4835v1
2009
Earlier work this paper cites.
B. P. Lanyon, J. D. Whitfield, G. G. Gillett, M. E. Goggin, M. P. Almeida, I. Kassal, J. D. Biamonte, M. Mohseni, B. J. Powell, M. Barbieri, et al. , Towards quantum chemistry on a quantum computer, Nat. Chem. 2
2010
Earlier work this paper cites.
J. Duchi, E. Hazan, and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res. 12
2011
Earlier work this paper cites.
M. Shapiro and P. Brumer, Quantum Control of Molecular Processes (Wiley-VCH Verlag GmbH & Co. KGaA, 2011)
2011
Earlier work this paper cites.
T. Tieleman, G. Hinton, et al. , Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, COURSERA: Neural networks for machine learning 4
2012
Earlier work this paper cites.
C. Wang, An overview of spsa: recent development and applications (2020), arXiv:2012.06952
2012
Earlier work this paper cites.
A. Hirose, Complex-Valued Neural Networks , Vol. 400 (Springer Berlin Heidelberg, 2012)
2012
Earlier work this paper cites.
L. Sorber, M. V. Barel, and L. D. Lathauwer, Unconstrained optimization of real functions in complex variables, SIAM J. Opt. 22
2012
Cited alongside, same era.
K. Laiho, M. Avenhaus, and C. Silberhorn, Characteristics of displaced single photons attained via higher order factorial moments, New J. Phys. 14
2012
Cited alongside, same era.
A. M. Brańczyk, D. H. Mahler, L. A. Rozema, A. Darabi, A. M. Steinberg, and D. F. V. James, Self-calibrating quantum state tomography, New J. Phys. 14
2012
Cited alongside, same era.
C. R. Müller, B. Stoklasa, C. Peuntinger, C. Gabriel, J. Řeháček, Z. Hradil, A. B. Klimov, G. Leuchs, C. Marquardt, and L. L. Sánchez-Soto, Quantum polarization tomography of bright squeezed light, New J. Phys. 14
2012
Cited alongside, same era.
A. Chiuri, L. Mazzola, M. Paternostro, and P. Mataloni, Tomographic characterization of correlations in a photonic tripartite state, New J. Phys. 14
S. Endo, J. Sun, Y. Li, S. C. Benjamin, and X. Yuan, Variational quantum simulation of general processes, Phys. Rev. Lett. 125
2020
Later among the works it cites.
L. Zambrano, L. Pereira, S. Niklitschek, and A. Delgado, Estimation of pure quantum states in high dimension at the limit of quantum accuracy through complex optimization and statistical inference, Sci. Rep. 10
2020
Later among the works it cites.
H. Chen, L. Wossnig, S. Severini, H. Neven, and M. Mohseni, Universal discriminative quantum neural networks, Quantum Mach. Intell. 3
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.
M. Lubasch, J. Joo, P. Moinier, M. Kiffner, and D. Jaksch, Variational quantum algorithms for nonlinear problems, Phys. Rev. A 101
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2012
Cited alongside, same era.
S. Wallentowitz, B. Seifert, and S. Godoy, Local sampling of the quantum phase-space distribution of a continuous-wave optical beam, New J. Phys. 14
2012
Cited alongside, same era.
C. Sayrin, I. Dotsenko, S. Gleyzes, M. Brune, J. M. Raimond, and S. Haroche, Optimal time-resolved photon number distribution reconstruction of a cavity field by maximum likelihood, New J. Phys. 14
2012
Cited alongside, same era.
M. W. Mitchell, M. Koschorreck, M. Kubasik, M. Napolitano, and R. J. Sewell, Certified quantum non-demolition measurement of material systems, New J. Phys. 14
2012
Cited alongside, same era.
M. Guţă, T. Kypraios, and I. Dryden, Rank-based model selection for multiple ions quantum tomography, New J. Phys. 14
2012
Cited alongside, same era.
L. Zhang, A. Datta, H. B. Coldenstrodt-Ronge, X.-M. Jin, J. Eisert, M. B. Plenio, and I. A. Walmsley, Recursive quantum detector tomography, New J. Phys. 14
2012
Cited alongside, same era.
G. Brida, L. Ciavarella, P. Degiovanni, M. Genovese, L. Lolli, M. G. Mingolla, F. Piacentini, M. Rajteri, E. Taralli, and M. G. A. Paris, Quantum characterization of superconducting photon counters, New J. Phys. 14
2012
Cited alongside, same era.
A. Anis and A. I. Lvovsky, Maximum-likelihood coherent-state quantum process tomography, New J. Phys. 14
2012
Cited alongside, same era.
2020
Later among the works it cites.
K. J. Sung, J. Yao, M. P. Harrigan, N. C. Rubin, Z. Jiang, L. Lin, R. Babbush, and J. R. McClean, Using models to improve optimizers for variational quantum algorithms, Quantum Sci. Technol. 5
2020
Later among the works it cites.
W. Lavrijsen, A. Tudor, J. Muller, C. Iancu, and W. de Jong, Classical optimizers for noisy intermediate-scale quantum devices, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2020) pp. 267–277
2020
Later among the works it cites.
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al. , SciPy 1.0: fundamental algorithms for scientific computing in python, Nat. Methods 17
2020
Later among the works it cites.
I. Hamamura and T. Imamichi, Efficient evaluation of quantum observables using entangled measurements, npj Quan. Inf. 6
2020
Later among the works it cites.
S. Mangini, F. Tacchino, D. Gerace, C. Macchiavello, and D. Bajoni, Quantum computing model of an artificial neuron with continuously valued input data, Mach. Learn.: Sci. Technol. 1
2020
Later among the works it cites.
Z. Hou, J.-F. Tang, C. Ferrie, G.-Y. Xiang, C.-F. Li, and G.-C. Guo, Experimental realization of self-guided quantum process tomography, Phys. Rev. A 101
2020
Later among the works it cites.
J. Stokes, J. Izaac, N. Killoran, and G. Carleo, Quantum natural gradient, Quantum 4
2020
Later among the works it cites.
D. Wierichs, C. Gogolin, and M. Kastoryano, Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer, Phys. Rev. Res. 2
2020
Later among the works it cites.
X. Wang, Z. Song, and Y. Wang, Variational quantum singular value decomposition, Quantum 5
2021
Later among the works it cites.
M. Rambach, M. Qaryan, M. Kewming, C. Ferrie, A. G. White, and J. Romero, Robust and efficient high-dimensional quantum state tomography, Phys. Rev. Lett. 126
2021
Later among the works it cites.
A. Patterson, H. Chen, L. Wossnig, S. Severini, D. Browne, and I. Rungger, Quantum state discrimination using noisy quantum neural networks, Phys. Rev. Res. 3
2021
Later among the works it cites.
X. Xu, S. C. Benjamin, and X. Yuan, Variational circuit compiler for quantum error correction, Phys. Rev. Appl. 15
2021
Later among the works it cites.
M. P. Harrigan, K. J. Sung, M. Neeley, K. J. Satzinger, F. Arute, K. Arya, J. Atalaya, J. C. Bardin, R. Barends, S. Boixo, et al. , Quantum approximate optimization of non-planar graph problems on a planar superconducting processor, Nat. Phys. 17
2021
Later among the works it cites.
K. Kubo, Y. O. Nakagawa, S. Endo, and S. Nagayama, Variational quantum simulations of stochastic differential equations, Phys. Rev. A 103
2021
Later among the works it cites.
A. Arrasmith, M. Cerezo, P. Czarnik, L. Cincio, and P. J. Coles, Effect of barren plateaus on gradient-free optimization, Quantum 5
2021
Later among the works it cites.
O. V. Borzenkova, G. I. Struchalin, A. S. Kardashin, V. V. Krasnikov, N. N. Skryabin, S. S. Straupe, S. P. Kulik, and J. D. Biamonte, Variational simulation of schwinger's hamiltonian with polarization qubits, Appl. Phys. Lett. 118
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.
J. Gacon, C. Zoufal, G. Carleo, and S. Woerner, Simultaneous perturbation stochastic approximation of the quantum fisher information, Quantum 5
2021
Later among the works it cites.
B. van Straaten and B. Koczor, Measurement cost of metric-aware variational quantum algorithms, PRX Quantum 2
2021
Later among the works it cites.
G. Aleksandrowicz et al. , Qiskit: An open-source framework for quantum computing (2021)
2021
Later among the works it cites.
K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, et al. , Noisy intermediate-scale quantum algorithms, Rev. Mod. Phys. 94
2022
Closest in time.
J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, and J. Tennyson, The variational quantum eigensolver: A review of methods and best practices, Phys. Rep. 986
2022
Closest in time.
K. Wang, Z. Song, X. Zhao, Z. Wang, and X. Wang, Detecting and quantifying entanglement on near-term quantum devices, npj Quant. Inf. 8
2022
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A. D. Muñoz Moller, L. Pereira, L. Zambrano, J. Cortés-Vega, and A. Delgado, Variational determination of multiqubit geometrical entanglement in noisy intermediate-scale quantum computers, Phys. Rev. Appl. 18
2022
Closest in time.
E. Farhi, J. Goldstone, S. Gutmann, and L. Zhou, The quantum approximate optimization algorithm and the sherrington-kirkpatrick model at infinite size, Quantum 6
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P. García-Molina, J. Rodríguez-Mediavilla, and J. J. García-Ripoll, Quantum fourier analysis for multivariate functions and applications to a class of schrödinger-type partial differential equations, Phys. Rev. A 105
2022
Closest in time.
2022
Closest in time.
2022
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2022
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G. Agliardi and E. Prati, Optimal tuning of quantum generative adversarial networks for multivariate distribution loading, Quantum Rep. 4
2022
Closest in time.
Y. Yao, P. Cussenot, R. A. Wolf, and F. Miatto, Complex natural gradient optimization for optical quantum circuit design, Phys. Rev. A 105
2022
Closest in time.
2022
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D. Concha, L. Pereira, L. Zambrano, and A. Delgado, Training a quantum measurement device to discriminate unknown non-orthogonal quantum states, Sci. Rep. 13
2023
Closest in time.
X. Bonet-Monroig, H. Wang, D. Vermetten, B. Senjean, C. Moussa, T. Bäck, V. Dunjko, and T. E. O’Brien, Performance comparison of optimization methods on variational quantum algorithms, Phys. Rev. A 107
2023
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Q. Wang, Z. Zhang, K. Chen, J. Guan, W. Fang, J. Liu, and M. Ying, Quantum algorithm for fidelity estimation, IEEE Trans. Inf. Theory 69
2023
Closest in time.
J. Cortés-Vega, J. F. Barra, L. Pereira, and A. Delgado, Detecting entanglement of unknown states by violating the clauser–horne–shimony–holt inequality, Quantum Inf. Process. 22
2023
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J. Gidi, jgidi/complexspsa.jl: v0.2.1 (2023), Zenodo. https://doi.org/10.5281/zenodo.7613310
2023
Closest in time.