Exploring the quantum speed limit with computer games (original) (raw)
McLeod, P. & Dienes, Z. Do fielders know where to go to catch the ball or only how to get there? J. Exp. Psychol. Hum. Percept. Perform.22, 531–543 (1996) Article Google Scholar
Gigerenzer, G. & Todd, P. in Simple Heuristics That Make Us Smart (eds Gigerenzer, G., Todd, P. & The ABC Research Group) 3–34 (Oxford Univ. Press, 1999)
Cooper, S. et al. Predicting protein structures with a multiplayer online game. Nature466, 756–760 (2010) ArticleADSCAS Google Scholar
Lee, J. et al. RNA design rules from a massive open laboratory. Proc. Natl Acad. Sci. USA111, 2122–2127 (2014) ArticleADS Google Scholar
Kim, J. S. et al. Space-time wiring specificity supports direction selectivity in the retina. Nature509, 331–336 (2014) ArticleCAS Google Scholar
Weitenberg, C., Kuhr, S., Mølmer, K. & Sherson, J. Quantum computation architecture using optical tweezers. Phys. Rev. A84, 032322 (2011) ArticleADS Google Scholar
Mandelstam, L. & Tamm, I. The uncertainty relation between energy and time in non-relativistic quantum mechanics. J. Phys.9, 249–254 (1945) MathSciNetMATH Google Scholar
Caneva, T., Calarco, T., Fazio, R., Santoro, G. E. & Montangero, S. Speeding up critical system dynamics through optimized evolution. Phys. Rev. A84, 012312 (2011) ArticleADS Google Scholar
Gajdacz, M., Das, K. K., Arlt, J., Sherson, J. F. & Opatrný, T. Time limited optimal dynamics beyond the quantum speed limit. Phys. Rev. A92, 062106 (2015) ArticleADS Google Scholar
Monroe, C. Quantum information processing with atoms and photons. Nature416, 238–246 (2002) ArticleADSCAS Google Scholar
Lewenstein, M. et al. Ultracold atomic gases in optical lattices: mimicking condensed matter physics and beyond. Adv. Phys.56, 243–379 (2007) ArticleADS Google Scholar
Devitt, S. J., Munro, W. J. & Nemoto, K. Quantum error correction for beginners. Rep. Prog. Phys.76, 076001 (2013) ArticleADS Google Scholar
Rabitz, H., Hsieh, M. M. & Rosenthal, C. M. Quantum optimally controlled transition landscapes. Science303, 1998–2001 (2004) ArticleADSCAS Google Scholar
Sklarz, S. & Tannor, D. Loading a Bose-Einstein condensate onto an optical lattice: an application of optimal control theory to the nonlinear Schrödinger equation. Phys. Rev. A66, 053619 (2002) ArticleADS Google Scholar
Ugray, Z. et al. Scatter search and local NLP solvers: a multistart framework for global optimization. Inf. J. Comp.19, 328–340 (2007) ArticleMathSciNet Google Scholar
Caneva, T. et al. Optimal control at the quantum speed limit. Phys. Rev. Lett.103, 240501 (2009) ArticleADSCAS Google Scholar
Bilalić, M., Langner, R., Erb, M. & Grodd, W. Mechanisms and neural basis of object and pattern recognition: a study with chess experts. J. Exp. Psychol. Gen.139, 728–742 (2010) Article Google Scholar
Lintott, C. et al. Galaxy Zoo 1: data release of morphological classifications for nearly 900,000 galaxies. Mon. Not. R. Astron. Soc.410, 166–178 (2011) ArticleADS Google Scholar
Bakr, W. S. et al. Probing the superfluid-to-Mott insulator transition at the single-atom level. Science329, 547–550 (2010) ArticleADSCAS Google Scholar
Sherson, J. F. et al. Single-atom-resolved fluorescence imaging of an atomic Mott insulator. Nature467, 68–72 (2010) ArticleADSCAS Google Scholar
Weitenberg, C. et al. Single-spin addressing in an atomic Mott insulator. Nature471, 319–324 (2011) ArticleADSCAS Google Scholar
Kaufman, A. et al. Entangling two transportable neutral atoms via local spin exchange. Nature527, 208–211 (2015) ArticleADSCAS Google Scholar
De Chiara, G. et al. Optimal control of atom transport for quantum gates in optical lattices. Phys. Rev. A77, 052333 (2008) ArticleADS Google Scholar
Jäger, G., Reich, D. M., Goerz, M. H., Koch, C. P. & Hohenester, U. Optimal quantum control of Bose–Einstein condensates in magnetic microtraps: comparison of gradient-ascent-pulse-engineering and Krotov optimization schemes. Phys. Rev. A90, 033628 (2014) ArticleADS Google Scholar
Doria, P., Calarco, T. & Montangero, S. Optimal control technique for many-body quantum dynamics. Phys. Rev. Lett.106, 190501 (2011) ArticleADS Google Scholar
Caneva, T., Calarco, T. & Montangero, S. Chopped random-basis quantum optimization. Phys. Rev. A84, 022326 (2011) ArticleADS Google Scholar
Zahedinejad, E., Schirmer, S. & Sanders, B. C. Evolutionary algorithms for hard quantum control. Phys. Rev. A90, 032310 (2014) ArticleADS Google Scholar
Roslund, J. & Rabitz, H. Experimental quantum control landscapes: inherent monotonicity and artificial structure. Phys. Rev. A80, 013408 (2009) ArticleADS Google Scholar
Vuculescu, O. & Bergenholtz, C. How to solve problems with crowds: a computer-based simulation model. Creativity Innov. Manage.23, 121–136 (2014) Article Google Scholar
Lieberoth, A. et al. Getting humans to do quantum optimization—user acquisition, engagement and early results from the citizen cyberscience game Quantum Moves. Human Comput.1, 221–246 (2014) Article Google Scholar
Sauermann, H. & Franzoni, C. Crowd science user contribution patterns and their implications. Proc. Natl Acad. Sci. USA112, 679–684 (2015) ArticleADSCAS Google Scholar
Calarco, T., Dorner, U., Julienne, P., Williams, C. & Zoller, P. Quantum computations with atoms in optical lattices: marker qubits and molecular interactions. Phys. Rev. A70, 012306 (2004) ArticleADS Google Scholar
Anderlini, M. et al. Controlled exchange interaction between pairs of neutral atoms in an optical lattice. Nature448, 452–456 (2007) ArticleADSCAS Google Scholar
Jørgensen, N. B., Bason, M. G. & Sherson, J. F. One- and two-qubit quantum gates using superimposed optical-lattice potentials. Phys. Rev. A89, 032306 (2014) ArticleADS Google Scholar