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QNet: exploring deep learning for quantum code smell detection
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NETL
NSTL
Springer Nature
Quantum computing (QC) has surged as a burgeoning domain, driving the evolution of novel programming paradigms. Despite extensive exploration, a critical aspect remains underex-plored: detecting code smells in quantum programs (QPs). Code smells, indicative of potential maintenance challenges, have been extensively studied in classical programming but pose unique hurdles in the quantum realm due to inherent disparities caused by unstable states in QC. This paper proposes a novel approach leveraging deep learning (DL) techniques for detecting quantum code smells (QCS). A comprehensive ablation study compares DL methodologies, including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM), and proposes a hybrid model combining CNN, LSTM, and GRU layers. Critical research questions are posed regarding QCS influence on error rates in quantum computers, the relative impact of different QCS on quantum system performance, and the complementary behaviour of distinct DL models in QCS detection. Through manual curation of a labelled dataset comprising 136 open-source projects, quantum circuits are extracted and analyzed. When evaluated on the quantum dataset, this hybrid model outperforms single-layer models. Furthermore, a comparative analysis with a transfer learning (TL) approach employing a pre-trained Bidirectional Encoder Representation Transformers (BERT) model underscores the superiority of the proposed DL-based solution. The proposed model achieves an impressive accuracy rate of 92.86%, surpassing existing DL and TL approaches. In conclusion, this research demonstrates the potential of DL to identify QCS, with the hybrid model offering avenues for further discoveries in QCS detection using DL.
NETL
NSTL
Springer Nature
