# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 2 Background
### Quantum Computing  
Based on Boolean logic, traditional computing can only process data in one of two states. There is either \"on\" or \"off\" in these states. In a quantum computer, the numbers 0 and 1 can be represented by different fundamental particles, such as electrons or photons, depending on how they are charged or polarized. In the context of quantum computing, each of these particles is called a quantum bit or qubit [28].  
Quantum entanglement and superposition of states are the two concepts in quantum physics that are thought to be the most important. Owing to quantum entanglement, qubits can talk to each other even if they are physically far apart (not restricted to the speed of light). Even if the connected particles are far apart, they are bound to each other. When quantum superposition and interposition are used together, they significantly affect the amount of computing power required. Traditional computers can only store one of four possible binary configurations (00, 01, 10, or 11) at any given time. However, a 2-qubit registry can store all four qubits simultaneously because each qubit represents two integers. However, traditional computers can store only one of four possible binary configurations. When more qubits are used, the capacity increases in a manner proportional to the square of the number of qubits [28].  
Even though quantum machine learning (QML) has a lot of promise to make IoMT more secure, it is important to be aware of the challenges and limitations that will always come up during its implementation. First of all, smaller and medium-sized hospitals have a hard time buying and acquiring quantum computing technology because it is rare and expensive. Secondly, the settings for making and running QML algorithms are still in their early stages, just like quantum technology itself. There is a high entry barrier because the learning curve is steep and there are not any standard tools or guidelines. This is partially because of how hard quantum computing is to learn.  
Lastly, we need to deal with how private and safe our data is. Quantum cryptography gives a higher level of protection, but it is not completely foolproof. If it is not kept in good shape, sensitive information about patients could be leaked, which would violate the patient's right to privacy and could have legal consequences. When integrating quantum-based systems with traditional computer platforms, there may be problems with how well they work together. If QML is going to be used successfully for IoMT security, then future studies will have to focus on finding solutions to these problems.  
In quantum physics, a \"photon\" is the smallest amount of light that can behave in a certain manner. Therefore, someone cannot listen in on a conversation, obtain one-half of a photon, and then use that photon to figure out how much it is worth before letting it keep going. The two people in QKD who are honest with each other devise a plan to throw off an eavesdropper by making mistakes in their conversation, thought to have started the field or created it, was the first person to suggest and show that quantum mechanical properties could be used in communication if information bits could be physically specified [29]. Information can be encoded and sent using the spin of an electron, the manner in which a photon scatters, or a combination of these and other quantum properties.  
Table 1 provides a summary of the literature review and their research gaps. Quantum computing is considered a futuristic solution to IoT security using its significantly improved computational capacity and speeds--analyzing the detailed attack surface of the IoMT network, providing useful vulnerability assessments to IoMT applications. Quantum machine learning is of significant importance to IoMT [30] and we consider its usage in vulnerability assessments of the complex and large network of IoMT devices in the following section.