Abstract
Citizen science methodologies have over the past decade been applied with great success to help solve highly complex numerical challenges. Here, we take early steps in the quantum physics arena by introducing a citizen science game, Quantum Moves 2, and compare the performance of different optimization methods across three different quantum optimal control problems of varying difficulty. Inside the game, players can apply a gradient-based algorithm (running locally on their device) to optimize their solutions and we find that these results perform roughly on par with the best of the tested standard optimization methods performed on a computer cluster. In addition, cluster-optimized player seeds was the only method to exhibit roughly optimal performance across all three challenges. This highlights the potential for crowdsourcing the solution of future quantum research problems.
Highlights
Despite amazing advances in the past years, it has becoming increasingly clear that pattern-matching results from deep learning algorithms alone can be surprisingly brittle [1,2]
Based on the results presented in the previous section, we expect the existence of distinct solution strategies, i.e., families of solutions that have a similar functional shape and characteristics but possibly different durations [47]
We have presented Quantum Moves 2, a citizen-science game in which players act as seeding mechanisms and initial optimizers for quantum optimal control problems
Summary
Despite amazing advances in the past years, it has becoming increasingly clear that pattern-matching results from deep learning algorithms alone can be surprisingly brittle [1,2]. One key challenge in this approach is that it requires large-scale studies of human capacities, for example common sense and the development of rich cognitive models [3] Initial steps in this direction can be taken by exploring problems in research-relevant contexts, such as in the related fields of citizen science and collective intelligence [9,11], and detailed comparisons between human [6] and AI [13] performance are becoming feasible for problems such as protein folding. We analyze three distinct scientific challenges—i.e., optimization landscapes—where the performance of the hybrid approach (human-computer) is compared against standard methodologies from quantum optimal control and computer science In this sense, this work does not aim to present or promote an inimical competition between players and computer algorithms, 2643-1564/2021/3(1)/013057(22). (Q2) Can the player-generated solutions, in combination with concrete algorithms, provide an edge against fully algorithmic solutions, and how does that depend on the type and mathematical complexity of the problem?
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
