TY - JOUR

T1 - Remote optimization of an ultracold atoms experiment by experts and citizen scientists

AU - Heck, Robert

AU - Vuculescu, Oana

AU - Sorensen, Jens Jakob

AU - Zoller, Jonathan

AU - Andreasen, Morten G.

AU - Bason, Mark G.

AU - Ejlertsen, Poul

AU - Eliasson, Otto

AU - Haikka, Pinja

AU - Laustsen, Jens S.

AU - Nielsen, Laerke L.

AU - Mao, Andrew

AU - Mueller, Romain

AU - Napolitano, Mario

AU - Pedersen, Mads K.

AU - Thorsen, Aske R.

AU - Bergenholtz, Carsten

AU - Calarco, Tommaso

AU - Montangero, Simone

AU - Sherson, Jacob F.

PY - 2018/11/27

Y1 - 2018/11/27

N2 - We introduce a remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.

AB - We introduce a remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.

KW - citizen science

KW - optimal control

KW - ultracold atoms

KW - human problem solving

KW - closed-loop optimization

KW - QUANTUM

KW - ADAPTATION

U2 - 10.1073/pnas.1716869115

DO - 10.1073/pnas.1716869115

M3 - Journal article

VL - 115

SP - E11231-E11237

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 48

ER -