# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 3 Proposed Model and Research Framework  
In this section, the proposed model using an innovative quantum deep learning algorithm is introduced alongside the other state-of-the-art machine learning models (both traditional and quantum). The proposed model is evaluated against the other machine learning model in detecting security vulnerabilities in IoMT applications. Due to the massivity, heterogeneity, and high velocity, IoMT security is considered challenging and the experimental analysis shows that the proposed quantum machine learning model can attain favorable security assessments. The suggested system, which combines quantum computing with machine learning for the purpose of IoMT security evaluation, has several primary goals, the most important of which are optimizing data processing, boosting the accuracy of prediction, and beefing up security measures. The following constitute the primary components of the framework: Data Collection and Preprocessing: During this phase, data is collected from a wide variety of IoMT devices and then transmitted to a hub server. The readings from sensors, data from \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. No.** & **Citation** & **Methods** & **Advantages** & **Disadvantages** & **Research Gaps** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{[PERSON] et al. (2023) [1]} & A Very Lightweight & Predicts attacks on the Internet of & Due to its lightweight nature, it may not be able to withstand & Protocol \\  & & (2023) [1] & Communicating in an AI Framework & minimal network overhead. & complicated attacks. & Improvements for Countering \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{[PERSON] et al. (2022) [2]} & \multirow{4}{*}{IoT Security (2022) [2]} & Identifies flaws in the ZigBee network’s & Only works with ZigBee networks and the MQTT protocol; might not operate in other Internet of Things settings. & Exploring more protocols and configurations for the Internet of Things. \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{[PERSON] et al. (2022) [3]} & \multirow{4}{*}{Literature Review} & Offers an in-depth analysis of the risks associated with the Internet of Things. & Insufficient experimental confirmation or real-world application. & Investigations into the discovered weaknesses \\  & & & & & \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{[PERSON] et al. (2022) [5]} & Hybrid Deep Learning & The application of deep learning may & The proposed approach has lower computational needs. & \\  & & (2022) [5] & in preventing botnet attacks in the IIoT. & necessitate a lot of computing power. & There have to be experiments & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{[PERSON] et al. (2020) [6]} & Literature Survey & Not enough effort has been put into & There have to be experiments & \\  & & (2020) [6] & measures for IoMT & actual use or & performed on the surveyed IoMT & \\  & & transmissions. & & testing. & security techniques. & \\ \hline \multirow{4}{*}{6} & \multirow{4}{*}{[PERSON] et al. (2022) [7]} & \multirow{4}{*}{Overview and Case} & IoMT Case Studies & It is possible that there isn’t enough technical & Formulation of approaches to resolving the problem at hand. \\  & & & Studies & Technologies & depth or proposed solutions to the problems. & Formulation of approaches to resolving the problem at hand. \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{[PERSON] et al. (2020) [8]} & \multirow{4}{*}{Literature Review and Recommendations.} & Describes the challenges of safeguarding IoMT systems and offers solutions. & The suggestions might not have been tested or validated in practice. & Testing and implementation of the suggested changes. \\ \hline \multirow{4}{*}{8} & \multirow{4}{*}{[PERSON] et al. (2022) [9]} & \multirow{4}{*}{Systematic Review} & Pinpoints security holes to stop IoMT attacks. & It is possible that there are no workable fixes for the detected flaws. & Creating workable fixes for the reported flaws. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Literature Review and Research Gaps.