Quantum Moves

Last updated
Quantum Moves
Developer(s) AU Ideas Center for Community Driven Research, University of Aarhus [1] [2]
Initial release2012;12 years ago (2012)
Preview release
Operating system Cross-platform: Windows, macOS, Linux
Available inEnglish
Type Citizen science, online game, quantum physics
License Proprietary freeware for academic and non-profit use
Website www.scienceathome.org

Quantum Moves is an online citizen science simulation video game where players move quantum atoms. The game is part of the ScienceAtHome [3] umbrella project, developed by AU Ideas Center for Community Driven Research (CODER). [4] CODER aims to merge theoretical and experimental quantum research with online community efforts to explore the potential for online citizen science in this otherwise highly specialized field.

Contents

The objective of the game is to complete challenges that are simulations of logical operations in a quantum computer. The team behind the game are building a scalable quantum computer with a processor consisting of 300 atoms. Logical operations are performed by moving the atoms with optical tweezers. Moving atoms in a controlled way is a difficult task because the atom becomes excited and the atomic wave function delocalises. Approaching the presumed quantum speed limit is a huge challenge for quantum algorithms and the task that Quantum Moves players are asked to tackle.

Gameplay

How gameplay helps ScienceAtHome build a quantum computer

In Quantum Moves, the atomic wave function is represented as a sloshy liquid in an energy potential well created by the optical tweezers. Players control the depth and the horizontal location of the well, simulating the path on the optical tweezers. The wave function reacts to changes in the potential function as dictated by the Schrödinger equation leading to sloshing seen by the players. Players are asked to move the well without sloshing the atomic wave function too much. A path created by a player maps one-to-one to a solution of the Schrödinger equation. Top results of the game play are then used to provide guidance into the algorithm's search space, resulting in solutions superior to those found by the algorithm alone.

History

In 2012, the first version of the game was developed in the programming language MATLAB and tested in several high schools across Denmark. The feedback was positive, but there were many technical issues that made the interaction in the game cumbersome. In the summer of 2012, the game was translated into Java and the first version of Quantum Moves was released. Since then, Quantum Moves has been built in Unity multi-platform development engine and released in the App Store and Google Play for use in touch screen devices.

As of February 2017, Quantum Moves had been played over 8 million times by more than 200,000 players worldwide. In April 2016, the journal Nature published an article "Exploring the quantum speed limit with computer games", [5] detailing the analysis of one of the levels in Quantum Moves called BringHomeWater. A small fraction of players found "better solutions than the numerical optimization, albeit with imperfect fidelities" well below the applied success criterion of 99.9%. In addition, bulk analysis of player strategies revealed a purely algorithmic "few-parameter heuristic optimization method", HILO, that efficiently outperformed all player results and the standard algorithm, KASS. The article was later retracted (see below).

In 2018 Dries Sels demonstrated that not only the HILO algorithm but also "a simple stochastic local optimization method finds near-optimal solutions which outperform all players". [6] In 2019 Allan Grønlund presented results of a number of conventional algorithms that cast doubt on the validity of the KASS algorithm. [7] He subsequently discovered that the authors of the original Nature paper had made a sign error [8] in their implementation of the benchmarked optimization algorithm, which led to the retraction of the Nature paper in July 2020. [9]

Subsequent work [10] [ when? ] analyzed the players’ results in conjunction with results obtained from GRAPE and the stochastic ascent algorithms with a variety of seeding strategies (all free from the original numerical error). The in-game optimized solutions of the players "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." The investigated purely numerical algorithms all perform significantly worse on at least one of the challenges. Finally, the authors conclude that "player seeds show significant statistical advantages over random seeds in the limit of sparse sampling. This highlights the potential for crowdsourcing the solution of future quantum research problems." In their conclusion, the authors warn that "these results should only be understood as a necessary baseline study and a first demonstration for further exploration, and they should not be taken as a guarantee that player-based seeding is advantageous when comparing to increasingly complex algorithmic strategies."

Quantum Moves 2

The sequel game, Quantum Moves 2, was launched in 2018 in conjunction with the Danish ReGAME Cup designed to teach students via research-enabling, citizen science games. The sequel featured a broader range of scientific challenges than the original game, as well as a built-in optimizer and a challenge curve featuring algorithmic results to which players could compare their performance.

As of 2021, Quantum Moves 2 has been played by more than 3600 unique players.

Controversy and retraction

In 2020 the Nature article [5] where the findings were presented was retracted due to major errors in calculations, deeming the results of the article false and the game untrustworthy. [11] [12] Although the results of the article were contested since its release in 2016, its coordinator denied the claims from other scientists around the world which found the results non-satisfactory and unrealistic. The coordinator of the project continued presenting the data as true until 2020 where an internal investigation from the University of Aarhus [13] discovered there were problems in the way the equations were implemented, resulting in a mistake which deemed the findings false as other scientists have claimed since the release of the article in 2016. [14] The coordinator of the project was then subject to disciplinary measurements for academic misconduct and scientific malpractice [15] which include manipulating information, lack of scientific cooperation, and manipulation of funders and academic coordination for continue presenting the findings as truthful. [16] [17]

Related Research Articles

<span class="mw-page-title-main">Quantum computing</span> Technology that uses quantum mechanics

A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.

<span class="mw-page-title-main">Search algorithm</span> Any algorithm which solves the search problem

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This is a timeline of quantum computing.

<span class="mw-page-title-main">Optical tweezers</span> Scientific instruments

Optical tweezers are scientific instruments that use a highly focused laser beam to hold and move microscopic and sub-microscopic objects like atoms, nanoparticles and droplets, in a manner similar to tweezers. If the object is held in air or vacuum without additional support, it can be called optical levitation.

<span class="mw-page-title-main">Combinatorial optimization</span> Subfield of mathematical optimization

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In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is generally reserved for algorithms that seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement.

Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.

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<span class="mw-page-title-main">Quantum neural network</span> Quantum Mechanics in Neural Networks

Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation were published independently in 1995 by Subhash Kak and Ron Chrisley, engaging with the theory of quantum mind, which posits that quantum effects play a role in cognitive function. However, typical research in quantum neural networks involves combining classical artificial neural network models with the advantages of quantum information in order to develop more efficient algorithms. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer is still in a premature stage, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments.

Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions, by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete with many local minima; such as finding the ground state of a spin glass or the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori in 1998 though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.

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Adiabatic quantum computation (AQC) is a form of quantum computing which relies on the adiabatic theorem to perform calculations and is closely related to quantum annealing.

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<span class="mw-page-title-main">Quantum simulator</span> Simulators of quantum mechanical systems

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The Harrow–Hassidim–Lloyd algorithm or HHL algorithm is a quantum algorithm for numerically solving a system of linear equations, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd. The algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations.

<span class="mw-page-title-main">Quantum machine learning</span> Interdisciplinary research area at the intersection of quantum physics and machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs.

Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best solution to a problem from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize an error which depends on the solution: the optimal solution has the minimal error. Different optimization techniques are applied in various fields such as mechanics, economics and engineering, and as the complexity and amount of data involved rise, more efficient ways of solving optimization problems are needed. Quantum computing may allow problems which are not practically feasible on classical computers to be solved, or suggest a considerable speed up with respect to the best known classical algorithm.

<span class="mw-page-title-main">ScienceAtHome</span>

ScienceAtHome is a team of scientists, game developers, designers and visual artists based at Aarhus University, Denmark. ScienceAtHome does research on quantum physics, citizen science and gamification. ScienceAtHome also develops games that contribute to scientific research, and studies how humans interpret information to achieve results superior to some algorithmic approaches.

The current state of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era, characterized by quantum processors containing up to 1,000 qubits which are not advanced enough yet for fault-tolerance or large enough to achieve quantum advantage. These processors, which are sensitive to their environment (noisy) and prone to quantum decoherence, are not yet capable of continuous quantum error correction. This intermediate-scale is defined by the quantum volume, which is based on the moderate number of qubits and gate fidelity. The term NISQ was coined by John Preskill in 2018.

In quantum computing, the variational quantum eigensolver (VQE) is a quantum algorithm for quantum chemistry, quantum simulations and optimization problems. It is a hybrid algorithm that uses both classical computers and quantum computers to find the ground state of a given physical system. Given a guess or ansatz, the quantum processor calculates the expectation value of the system with respect to an observable, often the Hamiltonian, and a classical optimizer is used to improve the guess. The algorithm is based on the variational method of quantum mechanics.

References

  1. ↑ ScienceAtHome
  2. ↑ "Center for Community Driven Research". Archived from the original on April 2, 2015. Retrieved March 11, 2014.
  3. ↑ "Home". ScienceAtHome. Retrieved 2014-03-11.
  4. ↑ "People". ScienceatHome. Retrieved 2014-03-11.
  5. 1 2 Sørensen, J. J.; Pedersen, M. K.; Munch, M.; Haikka, P.; Jensen, J. H.; Planke, T.; Sherson, J. F. (2016). "Exploring the quantum speed limit with computer games". Nature. 532 (7598): 210–213. arXiv: 1506.09091 . Bibcode:2016Natur.532..210S. doi:10.1038/nature17620. PMID   27075097. S2CID   4465890. (Retracted, see doi:10.1038/s41586-020-2515-2, PMID   32699408,  Retraction Watch)
  6. ↑ Sels, Dries (2018). "Stochastic gradient ascent outperforms gamers in the Quantum Moves game". Physical Review A. 97 (4): 040302. arXiv: 1709.08766 . Bibcode:2018PhRvA..97d0302S. doi:10.1103/PhysRevA.97.040302. S2CID   118874743.
  7. ↑ Grønlund, Allan (2019). "Algorithms Clearly Beat Gamers at Quantum Moves. A Verification". arXiv: 1904.01008 [cs.OH].
  8. ↑ Grønlund, Allan (2020-03-12). "Explaining the poor performance of the KASS algorithm implementation". arXiv: 2003.05808 [math.OC].
  9. ↑ Sørensen, J. J.; Pedersen, M. K.; Munch, M.; Haikka, P.; Jensen, J. H.; Planke, T.; Sherson, J. F. (2020). "Retraction Note: Exploring the quantum speed limit with computer games". Nature. 584 (7821): 484. doi: 10.1038/s41586-020-2515-2 . PMID   32699408.
  10. ↑ Jensen, J.H.M; Gajdacz, S.Z; Czarkowski, J.H.; Weidner, C.; Rafner, J.; Sørensen, J.J; Mølmer, K; Sherson, J. F. (2021). "Crowdsourcing human common sense for quantum control". Physical Review Research. 3 (1): 013057. arXiv: 2004.03296 . Bibcode:2021PhRvR...3a3057J. doi:10.1103/PhysRevResearch.3.013057. S2CID   215238819.
  11. ↑ Lund, Michael; Holm, Line Tolstrup (2020-12-27). "Forskeren bag fejlen: »Jeg er overrasket over, hvor stærke følelser vores projekt har vakt«". Berlingske.dk (in Danish). Retrieved 2022-07-17.
  12. ↑ Lund, Michael; Holm, Line Tolstrup (2021-01-22). "Forsker bag fejlbehæftet artikel tog æren for at have fundet fejlen". Berlingske.dk (in Danish). Retrieved 2022-07-17.
  13. ↑ Lund, Michael; Holm, Line Tolstrup (2020-12-27). "Forskerfejl skabte hidsig debat på universitet: »Tonen har nok været usædvanlig hård«". Berlingske.dk (in Danish). Retrieved 2022-07-17.
  14. ↑ Lund, Michael; Holm, Line Tolstrup (2020-12-27). "Eksperter undrer sig over forløb bag forskerfejl: Det ser bestemt ikke kønt ud". Berlingske.dk (in Danish). Retrieved 2022-07-17.
  15. ↑ 174481@au.dk (18 February 2021). "26 forskere udtrykker i brev "dyb bekymring" over ledelsens håndtering og langsommelighed i sag om tvivlsom videnskabelig praksis". omnibus.au.dk (in Danish). Retrieved 2022-07-17.{{cite web}}: CS1 maint: numeric names: authors list (link)
  16. ↑ "Rettelse til Genstartudsendelsen " Et gok i nøden"". DR (in Danish). 2021-01-29. Retrieved 2022-07-17.
  17. ↑ Holm, Line Tolstrup; Lund, Michael (2020-12-31). "Carlsbergfondet forsvarer millionbevilling til forsker bag fatal fejl". Berlingske.dk (in Danish). Retrieved 2022-07-17.

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