# Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)
## 1 Introduction  
Smart devices can be used to improve a wide range of services in ubiquitous computing. The gadgets that make up the \"things\" in the Internet of Things (IoT) can exist in any household, company, and city. The services based on IoT bring benefits but also security vulnerabilities in the form of blind spots and increased attack surfaces [1]. Smart devices with security vulnerabilities can allow malicious users to infiltrate private computing networks. Most IoT devices are vulnerable to cyber-attacks because they are not equipped with sufficient security features. These IoT networks are vulnerable to several factors, such as technological limitations and the users associated with the IoT applications [2]. Firstly, there are security vulnerabilities in IoT devices on the market because of their hardware limitations. IoT devices can only perform so much processing; their specific purpose is to provide minimized computing power (and constrained energy usage). Therefore, there are limited options for stronger data protection and security reinforcement. Secondly, diversity in the IoT device types brings challenges in establishing security protocols applicable to all IoT devices [3, 4]. Most importantly, the lack of user control in IoT automation provides severe challenges to IoT security assurance [5]. As IoT applications are applied to various fields, including medical services (e.g., monitoring patients) where security is of paramount importance, proactive and preventative security assurance is vital in future applications of Internet of Medical Things (IoMTs) [6]. The purpose of this study is to identify practical solutions to these IoT security vulnerabilities. This paper focuses on the Internet of Medical Things (IoMT) [7] because the IoT devices in this sector can have significant impacts on human lives and their well-being, and IoMT often lacks the most fundamental security assurance [8]. In this article, IoMT security is first discussed. We describe several actual attacks carried out against commercial IoMT devices [9; 10]. We review the processors, communication protocols, and cryptographic hardware/software used in commercial IoMT applications. It is absolutely necessary, in order to ensure the safety of IoMT devices, that ethical and privacy concerns be taken into account during the process of developing the IoMT. IoMT devices manage an astounding quantity of private and sensitive health information for their users. As a direct consequence of this, maintaining the secrecy of this information is of the utmost significance. Patients, healthcare professionals, and regulators should all have complete access to and an understanding of IoMT and its operations because it is crucial from an ethical standpoint that they do. It is imperative that the specific data being used, the manner in which it is being processed, and the potential repercussions of improper data use all be made transparent. The likelihood of data bias, which could lead to unequal treatment for some patients, is another consideration that IoMT needs to take into account. Concerns regarding the privacy of patients revolve around the necessity of preventing unauthorized access to sensitive information and maintaining compliance with legislation such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) of the European Union. A number of potential remedies include using robust data encryption, stringent access limits, and anonymizing patient data whenever it is practicable to do so. There is also the possibility that IoMT networks will inadvertently divulge sensitive information. Additionally, there is the possibility that unauthorized access will be gained and data will be misused. Because of this, it is absolutely necessary to design and implement IoMT and its operation through (quantum) machine learning that includes tight protections, comprehensive testing, and regular monitoring, so that any possible issues may be located and resolved as soon as they appear.