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Smart city article details
| Title | Securing Smart Cities: Unraveling Quantum As A Service |
|---|---|
| ID_Doc | 47791 |
| Authors | Caivano D.; De Vincentiis M.; Pal A.; Ragone A. |
| Year | 2023 |
| Published | QP4SE 2023 - Proceedings of the 2nd International Workshop on Quantum Programming for Software Engineering, Co-located with: ESEC/FSE 2023 |
| DOI | http://dx.doi.org/10.1145/3617570.3617865 |
| Abstract | Smart Cities attract significant attention and investment from government and private entities, leading to their rapid development. With such growth in Smart Cities, data volume and diversity increase due to Internet of Things (IoT) sensors in devices. However, this abundance of data exposes millions of vulnerable devices to cyber threats, risking compromised security and sensitive information. To address these risks, Smart Cities utilize Intelligent Operations Centers (IOCs) equipped with Machine Learning (ML) algorithms. These algorithms continuously monitor and protect against security incidents. Although Quantum Computing (QC) has shown promise in Smart City applications, its usage as a service is still in the early stages. In this paper, we propose to investigate the utilization of Quantum as a Service (QaaS) to develop an architecture for securing a Smart City. Our approach employs Quantum Classifiers, QBoost from D-Wave Leap Quantum Cloud and Variational Quantum Classifier, and PegaSoS Quantum Support Vector Classifier from IBM Quantum Services. These provide real-time data classification and a user-friendly dashboard to display security incidents in the Smart City. Among the three quantum classifiers considered for the proposed architecture, QBoost performed the best both regarding quality and processing time. © 2023 ACM. |
| Author Keywords | Quantum as a service; Quantum Machine Learning; Security; Smart City |
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