Fetching the paper…
Reading the bibliography…
Control of quantum systems is a central element of high-precision experiments and the development of quantum technological applications.
A. P. Peirce, M. A. Dahleh, and H. Rabitz, “Optimal control of quantum-mechanical systems: Existence, numerical approximation, and applications,” Phys. Rev. A 37
1988
Earlier work this paper cites.
R. S. Judson and H. Rabitz, “Teaching lasers to control molecules,” Phys. Rev. Lett. 68
1992
Earlier work this paper cites.
J. C. Spall, “Multivariate stochastic approximation using a simultaneous perturbation gradient approximation,” IEEE Transactions on Automatic Control 37
1992
Earlier work this paper cites.
A. Assion, T. Baumert, M. Bergt, T. Brixner, B. Kiefer, V. Seyfried, M. Strehle, and G. Gerber, “Control of chemical reactions by feedback-optimized phase-shaped femtosecond laser pulses,” Science 282
1998
Earlier work this paper cites.
M. Greiner, O. Mandel, T. Esslinger, T. W. Hänsch, and I. Bloch, “Quantum phase transition from a superfluid to a mott insulator in a gas of ultracold atoms,” Nature 415
2002
Earlier work this paper cites.
C. F. Roos, M. Riebe, H. Häffner, W. Hänsel, J. Benhelm, G. P. T. Lancaster, C. Becher, F. Schmidt-Kaler, and R. Blatt, “Control and measurement of three-qubit entangled states,” Science 304
2004
Earlier work this paper cites.
D. Leibfried, M. D. Barrett, T. Schaetz, J. Britton, J. Chiaverini, W. M. Itano, J. D. Jost, C. Langer, and D. J. Wineland, “Toward heisenberg-limited spectroscopy with multiparticle entangled states,” Science 304
2004
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. Mag. Res. 172
2005
Earlier work this paper cites.
C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning , Vol. 2 (MIT press Cambridge, MA, 2006)
2006
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.
H. Nickisch and C. Rasmussen, “Approximations for binary gaussian process classification,” Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
W. S. Bakr, J. I. Gillen, A. Peng, S. Fölling, and M. Greiner, “A quantum gas microscope for detecting single atoms in a hubbard-regime optical lattice,” Nature 462
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
F. Platzer, F. Mintert, and A. Buchleitner, “Optimal dynamical control of many-body entanglement,” Phys. Rev. Lett. 105
2010
Earlier work this paper cites.
L. DiCarlo, M. D. Reed, L. Sun, B. R. Johnson, J. M. Chow, J. M. Gambetta, L. Frunzio, S. M. Girvin, M. H. Devoret, and R. J. Schoelkopf, “Preparation and measurement of three-qubit entanglement in a superconducting circuit,” Nature 467
2010
Earlier work this paper cites.
M. Neeley, R. C. Bialczak, M. Lenander, E. Lucero, M. Mariantoni, A. D. O’Connell, D. Sank, H. Wang, M. Weides, J. Wenner, Y. Yin, T. Yamamoto, A. N. Cleland, and J. M. Martinis, “Generation of three-qubit entangled states using superconducting phase qubits,” Nature 467
2010
Earlier work this paper cites.
H. Katori, “Optical lattice clocks and quantum metrology,” Nature Photonics 5
2011
Earlier work this paper cites.
T. Caneva, T. Calarco, and S. Montangero, “Chopped random-basis quantum optimization,” Phys. Rev. A 84
2011
Earlier work this paper cites.
A. Sergeevich, A. Chandran, J. Combes, S. D. Bartlett, and H. M. Wiseman, “Characterization of a qubit hamiltonian using adaptive measurements in a fixed basis,” Phys. Rev. A 84
2011
Earlier work this paper cites.
A. S. Holevo, Probabilistic and statistical aspects of quantum theory , Vol. 1 (Springer Science & Business Media, 2011)
2011
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, “Advances in quantum metrology,” Nature Photonics 5
2011
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Advances in neural information processing systems (2012) pp. 2951–2959
2012
Earlier work this paper cites.
GPy, “GPy: A gaussian process framework in python,” http://github.com/SheffieldML/GPy (2012)
2012
Earlier work this paper cites.
C. Monroe and J. Kim, “Scaling the ion trap quantum processor,” Science 339
2013
Earlier work this paper cites.
S. Rosi, A. Bernard, N. Fabbri, L. Fallani, C. Fort, M. Inguscio, T. Calarco, and S. Montangero, “Fast closed-loop optimal control of ultracold atoms in an optical lattice,” Phys. Rev. A 88
2013
Earlier work this paper cites.
J. Johansson, P. Nation, and F. Nori, “Qutip 2: A python framework for the dynamics of open quantum systems,” Computer Physics Communications 184
2013
Earlier work this paper cites.
R. Barends, J. Kelly, A. Megrant, A. Veitia, D. Sank, E. Jeffrey, T. C. White, J. Mutus, A. G. Fowler, B. Campbell, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, C. Neill, P. O’Malley, P. Roushan, A. Vainsencher, J. Wenner, A. N. Korotkov, A. N. Cleland, and J. M. Martinis, “Superconducting quantum circuits at the surface code threshold for fault tolerance,” Nature 508
2014
Earlier work this paper cites.
J. Kelly, R. Barends, B. Campbell, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, A. G. Fowler, I.-C. Hoi, E. Jeffrey, A. Megrant, J. Mutus, C. Neill, P. J. J. O’Malley, C. Quintana, P. Roushan, D. Sank, A. Vainsencher, J. Wenner, T. C. White, A. N. Cleland, and J. M. Martinis, “Optimal quantum control using randomized benchmarking,” Phys. Rev. Lett. 112
2014
Earlier work this paper cites.
D. J. Egger and F. K. Wilhelm, “Adaptive hybrid optimal quantum control for imprecisely characterized systems,” Phys. Rev. Lett. 112
2014
Earlier work this paper cites.
E. Zahedinejad, S. Schirmer, and B. C. Sanders, “Evolutionary algorithms for hard quantum control,” Phys. Rev. A 90
2014
Cited alongside, same era.
F. Dolde, V. Bergholm, Y. Wang, I. Jakobi, B. Naydenov, S. Pezzagna, J. Meijer, F. Jelezko, P. Neumann, T. Schulte-Herbrüggen, J. Biamonte, and J. Wrachtrup, “High-fidelity spin entanglement using optimal control,” Nat. Commun. 5
2014
Cited alongside, same era.
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,” Nat. Commun. 5
2014
Cited alongside, same era.
2014
Cited alongside, same era.
A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,” Nature 549
2017
Later among the works it cites.
P. Weinberg and M. Bukov, “QuSpin: a Python Package for Dynamics and Exact Diagonalisation of Quantum Many Body Systems part I: spin chains,” SciPost Phys. 2
2017
Later among the works it cites.
D. S. Weiss and M. Saffman, “Quantum computing with neutral atoms,” Physics Today 70
2017
Later among the works it cites.
H. Bernien, S. Schwartz, A. Keesling, H. Levine, A. Omran, H. Pichler, S. Choi, A. S. Zibrov, M. Endres, M. Greiner, V. Vuletic, and M. D. Lukin, “Probing many-body dynamics on a 51-atom quantum simulator,” Nature 551
2017
Later among the works it cites.
C. L. Degen, F. Reinhard, and P. Cappellaro, “Quantum sensing,” Rev. Mod. Phys. 89
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. D. Shulman, S. P. Harvey, J. M. Nichol, S. D. Bartlett, A. C. Doherty, V. Umansky, and A. Yacoby, “Suppressing qubit dephasing using real-time hamiltonian estimation,” Nat. Commun. 5
2014
Cited alongside, same era.
C. Ferrie and O. Moussa, “Robust and efficient in situ quantum control,” Phys. Rev. A 91
2015
Cited alongside, same era.
B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas, “Taking the human out of the loop: A review of bayesian optimization,” Proceedings of the IEEE 104
2015
Cited alongside, same era.
S. J. Glaser, U. Boscain, T. Calarco, C. P. Koch, W. Köckenberger, R. Kosloff, I. Kuprov, B. Luy, S. Schirmer, T. Schulte-Herbrüggen, D. Sugny, and F. K. Wilhelm, “Training schrödinger’s cat: quantum optimal control,” Eur. Phys. J. D 69
2015
Cited alongside, same era.
J. Cui and F. Mintert, “Robust control of long-distance entanglement in disordered spin chains,” New J. Phys. 17
2015
Cited alongside, same era.
T. Nöbauer, A. Angerer, B. Bartels, M. Trupke, S. Rotter, J. Schmiedmayer, F. Mintert, and J. Majer, “Smooth optimal quantum control for robust solid-state spin magnetometry,” Phys. Rev. Lett. 115
2015
Cited alongside, same era.
M. Schreiber, S. S. Hodgman, P. Bordia, H. P. Lüschen, M. H. Fischer, R. Vosk, E. Altman, U. Schneider, and I. Bloch, “Observation of many-body localization of interacting fermions in a quasirandom optical lattice,” Science 349
2015
Cited alongside, same era.
Z. Ghahramani, “Probabilistic machine learning and artificial intelligence,” Nature (London) 521
2015
Cited alongside, same era.
2017
Later among the works it cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” in Advances in Neural Information Processing Systems (2017) pp. 6402–6413
2017
Later among the works it cites.
B. Dive, A. Pitchford, F. Mintert, and D. Burgarth, “In situ upgrade of quantum simulators to universal computers,” Quantum 2
2018
Later among the works it cites.
G. Feng, F. H. Cho, H. Katiyar, J. Li, D. Lu, J. Baugh, and R. Laflamme, “Gradient-based closed-loop quantum optimal control in a solid-state two-qubit system,” Phys. Rev. A 98
2018
Later among the works it cites.
F. Poggiali, P. Cappellaro, and N. Fabbri, “Optimal control for one-qubit quantum sensing,” Phys. Rev. X 8
2018
Later among the works it cites.
M. Bukov, A. G. R. Day, D. Sels, P. Weinberg, A. Polkovnikov, and P. Mehta, “Reinforcement learning in different phases of quantum control,” Phys. Rev. X 8
2018
Later among the works it cites.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,” Nat. Commun. 9
2018
Later among the works it cites.
A. A. Melnikov, H. Poulsen Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger, and H. J. Briegel, “Active learning machine learns to create new quantum experiments,” Proc. Natl. Acad. Sci. U.S.A. 115
2018
Later among the works it cites.
T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, “Reinforcement learning with neural networks for quantum feedback,” Phys. Rev. X 8
2018
Later among the works it cites.
M. August and J. M. Hernández-Lobato, “Taking gradients through experiments: Lstms and memory proximal policy optimization for black-box quantum control,” in International Conference on High Performance Computing (Springer, 2018) pp. 591–613
2018
Later among the works it cites.
P. I. Frazier, “A tutorial on bayesian optimization,” arXiv:1807.02811 (2018)
2018
Later among the works it cites.
B. M. Henson, D. K. Shin, K. F. Thomas, J. A. Ross, M. R. Hush, S. S. Hodgman, and A. G. Truscott, “Approaching the adiabatic timescale with machine learning,” Proc. Nat. Acad. Sci. U.S.A. 115
2018
Later among the works it cites.
S. Machnes, E. Assémat, D. Tannor, and F. K. Wilhelm, “Tunable, flexible, and efficient optimization of control pulses for practical qubits,” Phys. Rev. Lett. 120
2018
Later among the works it cites.
T. L. Nguyen, J. M. Raimond, C. Sayrin, R. Cortiñas, T. Cantat-Moltrecht, F. Assemat, I. Dotsenko, S. Gleyzes, S. Haroche, G. Roux, T. Jolicoeur, and M. Brune, “Towards quantum simulation with circular rydberg atoms,” Phys. Rev. X 8
2018
Later among the works it cites.
A. D. Tranter, H. J. Slatyer, M. R. Hush, A. C. Leung, J. L. Everett, K. V. Paul, P. Vernaz-Gris, P. K. Lam, B. C. Buchler, and G. T. Campbell, “Multiparameter optimisation of a magneto-optical trap using deep learning,” Nat. Commun. 9
2018
Later among the works it cites.
M. Y. Niu, S. Boixo, V. N. Smelyanskiy, and H. Neven, “Universal quantum control through deep reinforcement learning,” npj Quantum Inf. 5
2019
Closest in time.
D. Zhu, N. M. Linke, M. Benedetti, K. A. Landsman, N. H. Nguyen, C. H. Alderete, A. Perdomo-Ortiz, N. Korda, A. Garfoot, C. Brecque, L. Egan, O. Perdomo, and C. Monroe, “Training of quantum circuits on a hybrid quantum computer,” Science Advances 5
2019
Closest in time.
I. Nakamura, A. Kanemura, T. Nakaso, R. Yamamoto, and T. Fukuhara, “Non-standard trajectories found by machine learning for evaporative cooling of 87 rb atoms,” Optics express 27
2019
Closest in time.
2019
Closest in time.
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature 567
2019
Closest in time.
K. Wright, K. M. Beck, S. Debnath, J. M. Amini, Y. Nam, N. Grzesiak, J.-S. Chen, N. C. Pisenti, M. Chmielewski, C. Collins, K. M. Hudek, J. Mizrahi, J. D. Wong-Campos, S. Allen, J. Apisdorf, P. Solomon, M. Williams, A. M. Ducore, A. Blinov, S. M. Kreikemeier, V. Chaplin, M. Keesan, C. Monroe, and J. Kim, “Benchmarking an 11-qubit quantum computer,” Nat. Commun. 10
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
R. Mukherjee, F. Sauvage, H. Xie, R. Loew, and F. Mintert, “Preparation of ordered states in ultra-cold gases using bayesian optimization,” New J. Phys. (2020)
2020
Closest in time.