# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 3 Proposed Model and Research Framework
### Experimental Comparisons  
We experimented with several machine-learning models. These models were constructed using Python 3.7.0, TensorFlow 1.10.0, and the Keras library on the web-based interactive computing platform Jupyter Notebook, version 5.6.0. The ML models were then separated using multi-class and binary-class methods. This resulted in two copies of the final labeled dataset as presented in Table 2.  
The following is a list of all of the parameters for the fields:  
1. IoMT devices are what the name \"IoMT Device\" refers to in its most specific form. 2. The IoMT Device Type indicates the broad category that the device belongs to in its entirety (i.e., biometric, therapeutic, implantable, etc.) 3. Network Type: The configuration of the device's network at the present time (i.e., ZigBee, Wi-Fi, Bluetooth, Cellular, etc.) 4. The speed in megabits per second (Mbps) at which the device can send and receive data. 5. Implementation of the Quantum ML Method: When assessing the safety of a network, specialists frequently use quantum machine learning techniques such as Quantum Support Vector Machine (SVM), Quantum Neural Network, Quantum Decision Tree, and a variety of other approaches. 6. Type of Security Threat Identified: Specifies the type of security threat, if any, that was determined to have been identified (i.e., Data Tampering, Denial of Service, Eavesdropping, No Threat, etc.) 7. The level of danger that the threat poses can be characterized as \"High\", \"Medium\", \"Low\", or \"Not Applicable\". 8. This metric will read \"Correct Detection\" (Yes/No) depending on whether the quantum ML algorithm accurately identified the threat or the absence of such a threat. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**IoT Device**} & **IoMT Device** & **Network** & **Data Transmission** & **Quantum Machine** & **Detected Security** & **Threat** & **Correct Detection** \\  & & **Type** & **Type** & **Rate** & **Learning Used** & **Threats** & **Severity** & **(Yes or No)** \\ \hline
1 & Heart Rate Monitor & Biometric & ZigBee & 100 Kpps & Quantum SVM & Data Tampering & High & Yes \\ \hline
2 & Insulin Pump & Therapeutic & Wi-Fi & 11 Mbps & Quantum Neural Network & Denial of Service & Medium & Yes \\ \hline
3 & Smart Inhaler & Therapeutic & Bluetooth & 2 Mbps & Quantum Decision Tree & No Threat & N/A & Yes \\ \hline
4 & Pacemaker & Implantable & Cellular & 1 Mbps & Quantum SVM & Eavesdropping & High & No \\ \hline
5 & Fitness Tracker & Biometric & Bluetooth & 3 Mbps & Quantum Neural Network & No Threat & N/A & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Datasets and Threat Information. This dataset may be given with real or simulated data, depending on the resources that are available and the ethical considerations that must be taken into account. The first one for testing is the quantum machine learning multi-class. The labels indicate whether the code is a security risk or not. There are some reasons you might want to use Quantum Machine learning (QML) classification. It is easier for the developer to keep up with vulnerable code if it is first put into the QML. This makes sense because QML has already been used as a standard for identifying vulnerabilities. We used binary classification to compare our experimental outcomes with those of other studies in the field that used the same method. An experiment under controlled conditions was carried out, using data gathered from IoMT devices already in operation. It was determined whether or not the proposed Quantum-Deep Learning (Q-Deep Learning) model was capable of efficiently locating vulnerabilities in the IoMT infrastructure by putting it through its paces in a series of tests. For the purpose of this inquiry, data was gathered from a diverse assortment of medical devices and computer systems located in several different types of healthcare facilities. IBM's quantum machine learning capabilities were utilized throughout the tests.