  One of the most important challenges in the field of software code audit is
the presence of vulnerabilities in software source code. These flaws are highly
likely ex-ploited and lead to system compromise, data leakage, or denial of
ser-vice. C and C++ open source code are now available in order to create a
large-scale, classical machine-learning and quantum machine-learning system for
function-level vulnerability identification. We assembled a siz-able dataset of
millions of open-source functions that point to poten-tial exploits. We created
an efficient and scalable vulnerability detection method based on a deep neural
network model Long Short Term Memory (LSTM), and quantum machine learning model
Long Short Term Memory (QLSTM), that can learn features extracted from the
source codes. The source code is first converted into a minimal intermediate
representation to remove the pointless components and shorten the de-pendency.
Therefore, We keep the semantic and syntactic information using state of the
art word embedding algorithms such as Glove and fastText. The embedded vectors
are subsequently fed into the classical and quantum convolutional neural
networks to classify the possible vulnerabilities. To measure the performance,
we used evaluation metrics such as F1 score, precision, re-call, accuracy, and
total execution time. We made a comparison between the results derived from the
classical LSTM and quantum LSTM using basic feature representation as well as
semantic and syntactic represen-tation. We found that the QLSTM with semantic
and syntactic features detects significantly accurate vulnerability and runs
faster than its classical counterpart.
