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

Heck, Robert; Vuculescu, Oana; Sørensen, Jens Jakob; Zoller, Jonathan K.; Andreasen, Morten Ginnerup; Bason, M. G.; Ejlertsen, Poul; Elíasson, Ottó; Haikka, Pinja; Laustsen, Jens S.; Nielsen, Lærke L; Mao, Andrew; Müller, Romain; Napolitano, M.; Pedersen, Mads Kock; Thorsen, Aske; Bergenholtz, Carsten; Calarco, Tommaso; Montangero, Simone; Sherson, Jacob

doi:10.1073/pnas.1716869115

Cited by 69 publications

(79 citation statements)

References 54 publications

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“…Trapped neutral atoms are attractive building blocks for large scale quantum information systems. They can be readily manipulated in large numbers while maintaining excellent quantum coherence, as has been demonstrated in remarkable quantum simulation and precision measurement experiments [1,2]. Single atom initialization, addressing, and readout have been demonstrated in a variety of optical trapping platforms, and single-qubit gates have been implemented with exquisite fidelity [3][4][5].…”

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confidence: 99%

Parallel Implementation of High-Fidelity Multiqubit Gates with Neutral Atoms

et al. 2019

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We report the implementation of universal two-and three-qubit entangling gates on neutral atom qubits encoded in long-lived hyperfine ground states. The gates are mediated by excitation to strongly interacting Rydberg states, and are implemented in parallel on several clusters of atoms in a one-dimensional array of optical tweezers. Specifically, we realize the controlled-phase gate, enacted by a novel, fast protocol involving only global coupling of two qubits to Rydberg states. We benchmark this operation by preparing Bell states with fidelity F ≥ 95.0(2)%, and extract gate fidelity ≥ 97.4(3)%, averaged across five atom pairs. In addition, we report a proof-of-principle implementation of the three-qubit Toffoli gate, in which two control atoms simultaneously constrain the behavior of one target atom. These experiments demonstrate key ingredients for high-fidelity quantum information processing in a scalable neutral atom platform.

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confidence: 99%

Parallel Implementation of High-Fidelity Multiqubit Gates with Neutral Atoms

et al. 2019

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“…As CT4TD relies on the RedCRAB optimization framework [10], the overall procedure could be implemented in remote, paving the way for a wireless decision support system for therapy design, to be used directly by clinicians [62]. In this respect, as a future development, an open-source computational tool will be made available to the scientific community, allowing to perform individual-specific analysis for a wide range of disease.…”

Section: Discussionmentioning

confidence: 99%

“…The CT4TD framework aims at improving the current state-of-the-art, by solving an open-loop control problem on PK/PD models via RedCRAB (Remote dressed Chopped Random Basis) [43,44,10], a gradient-free framework for optimization and control. Extensions to the closed-loop case are underway.…”

Section: Applications Of Optimal Control Theory In Medicinementioning

confidence: 99%

“…We here considered two terms, in order to: (i) minimize the tumor burden (weight W 1 ), and (ii) minimize the AUC (weight W 2 ) (see Materials and Methods for further details). We scanned the values of φ = W1 W2 in the range [10,100], by repeatedly applying the CT4TD framework to the 22-patients CML dataset from [6]. (A).…”

Section: Adjusting Treatment For Tumor Burden Reductionmentioning

confidence: 99%

“…Here, we introduce a theoretical framework named CT4TD (Control Theory for Therapy Design), which employs the RedCRAB optimal control algorithm [10], on patient-specific pharmacokinetics and pharmacodynamics (PK/PD) models [11], with the goal of delivering an optimized drug administration schedule (see Figure 1 for a schematic representation of the framework).…”

Section: Introductionmentioning

confidence: 99%

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Personalized Therapy Design for Liquid Tumors via Optimal Control Theory

Angaroni

Graudenzi

Rossignolo

et al. 2019

Preprint

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One of the key challenges in current cancer research is the development of reliable methods for the definition of personalized therapeutic strategies, based on increasingly available experimental data on single patients. To this end, methods from control theory can be effectively employed on patient-specific pharmacokinetic and pharmacodynamic models to generate robust data-driven experimental hypotheses. Here we introduce the Control Theory for Therapy Design (CT4TD) theoretical framework for the generation of optimized personalized therapeutic strategies in cancer patients, based on optimal control theory and population dynamics modeling. The CT4TD framework can help clinicians in designing patient-specific therapeutic regimens, with the specific goal of optimizing the efficacy of the cure while reducing the costs, especially in terms of toxicity and adverse effects. CT4TD can be used at the time of the diagnosis in order to set optimized personalized therapies to reach selected target drug concentrations. Furthermore, if longitudinal data on patients under treatment are available, our approach introduces the possibility of adjusting the therapy with the explicit goal of minimizing the tumor burden measured in each case. As a case study, we present the application of CT4TD to Imatinib administration in Chronic Myeloid A PREPRINT -JUNE 7, 2019 Leukemia, in which we show that the optimized therapeutic strategies are extremely diversified among patients, and display improvements with respect to the currently employed regimes. Interestingly, we prove that much of the variance in therapeutic response observed among patients is due to the individual differences in pharmacokinetics, rather than in pharmacodynamics.

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Exploration and exploitation in complex search tasks: How feedback influences whether and where human agents search

Billinger

Srikanth

Stieglitz

et al. 2020

Strategic Management Journal

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Research Abstract Exploration and exploitation in strategic decision‐making entails decisions about whether and where to search for new alternatives to improve the status quo. Prior research has not explored how decisions about whether to continue search (vs. stop search or satisfice) and where to search (near vs. far) are interrelated. We report laboratory experiment results on how individuals decide whether and where to search in a complex, combinatorial task. We find that different feedback variables influence the decision to stop search from decisions regarding how broadly to search. Our results suggest that not accounting for the decision to continue (or stop) searching, separately from breadth of search, can lead to incorrect predictions regarding how feedback influences search behavior. Managerial Abstract Managers concerned about the performance of their company face two challenges—they have to find out what potential performance is feasible given their business environment and which organizational policies to implement to realize it. We use a stylized laboratory experiment to better understand how feedback from experimentation informs such a learning process. We find that early‐stage feedback has a lasting impact on performance aspirations and managerial expectations about feasible performance. Superior early feedback thereby motivates more sustained experimentation with organizational policies. In contrast, more recent feedback guides the extent to which managers are willing to engage in more radical policy adjustments, especially in the latter stages of a learning process.

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

Cited by 69 publications

References 54 publications

Parallel Implementation of High-Fidelity Multiqubit Gates with Neutral Atoms

Parallel Implementation of High-Fidelity Multiqubit Gates with Neutral Atoms

Personalized Therapy Design for Liquid Tumors via Optimal Control Theory

Exploration and exploitation in complex search tasks: How feedback influences whether and where human agents search

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