Exploring the Quantum Speed Limit with Computer Games

TL;DR

This paper introduces Quantum Moves, a gamified platform where non-expert players solve complex quantum optimization problems, revealing new heuristic strategies that outperform traditional numerical methods and deepen understanding of quantum speed limits.

Contribution

The study demonstrates that citizen scientists can effectively solve quantum optimization problems through gamification, leading to novel heuristic methods and insights into quantum control landscapes.

Findings

Humans outperform numerical methods in quantum optimization tasks.

Player strategies inform the development of efficient heuristic algorithms.

Analysis of solutions reveals why traditional methods struggle near the quantum speed limit.

Abstract

Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies. Citizen science exploits this ability by presenting scientific research problems to non-experts. Gamification is an effective tool for attracting citizen scientists to provide solutions to research problems. While citizen science games Foldit, EteRNA and EyeWire have been used successfully to study protein and RNA folding and neuron mapping, so far gamification has not been applied to problems in quantum physics. Does the fact that everyday experiences are based on classical physics hinder the use of non-expert citizen scientists in the realm of quantum mechanics? Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics. We show that human players are able to find solutions to difficult problems associated with the task of quantum computing. Players succeed where purely numerical optimization fails, and analyses of their solutions provide insights into the problem of optimization of a more profound and general nature. Based on player strategies, we have thus developed a new, few-parameter heuristic optimization method which efficiently outperforms the most prominent established numerical methods. The numerical complexity associated with time-optimal solutions increases for shorter process durations. To better understand this, we have made a low-dimensional rendering of the optimization landscape. These studies show why traditional optimization methods fail near the quantum speed limit, and they bring promise that combined analyses of optimization landscapes and heuristic solution strategies may benefit wider classes of optimization problems in quantum physics and beyond.