A Deep-Learning-Based Proposal to Aid Users in Quantum Computing Programming

  • Juan Cruz-BenitoEmail author
  • Ismael Faro
  • Francisco Martín-Fernández
  • Roberto Therón
  • Francisco J. García-Peñalvo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10925)


New languages like Open QASM and SDKs like QISKit open new horizons for the research and development in the new paradigm of quantum computing. Despite that, they present an evident learning curve that could be hard for regular developers and newcomers in the field of quantum computing. On the other hand, currently there are many ways to build intelligent systems that can learn from humans and processes to build a knowledge corpus and provide a different kind of help to humans in tasks like aiding in decision making processes, recommending multimedia resources, building conversational agents, etc. In this paper we describe a work-in-progress project developed by the IBM Q team that implements an intelligent system based on a deep learning approach that learns how people code using the Open QASM language to later offer help and guidance to the coders by recommending different code sequences, logical steps or even small pieces of code. During the paper, we describe our current approach and first results. They include the use of seq2seq neural networks that effectively learn quantum-code sequences, and which will be tested in real context in the near future to improve the user experience in IBM Q Experience products.


Deep learning Artificial intelligence Quantum computing Programming Open QASM QISKit 



We thank to the ACQX (AI Challenges & Quantum Experience Team at IBM Research) and to the GRIAL research group team for the useful discussions and the feedback received.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Juan Cruz-Benito
    • 1
    Email author
  • Ismael Faro
    • 1
  • Francisco Martín-Fernández
    • 1
  • Roberto Therón
    • 2
  • Francisco J. García-Peñalvo
    • 2
  1. 1.IBM Research. T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.GRIAL Research Group, Department of Computer ScienceUniversity of SalamancaSalamancaSpain

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