QP4SE '23: "On the Need of Quantum-Oriented ..."
On the Need of Quantum-Oriented Paradigm
Shaukat Ali and Tao Yue
(Simula Research Laboratory, Norway; Oslo Metropolitan University, Norway)
Since the invention of Quantum Computing (QC) in the 1980s, substantial claims about QC’s ability to solve computational problems of unparalleled complexity have emerged. However, forty years later, no significant real-world QC applications exist. Indeed, the availability of small-scale noisy quantum computers is to blame. Still, simultaneously, the programming of quantum computers is too close to quantum hardware, requiring software engineers with specialized backgrounds to build QC applications and limiting the maximum exploitation of QC’s potential. Thus, there is a need for an abstract yet intuitive quantum-oriented paradigm (QOP) for building QC applications, similar to the object-oriented paradigm established in the 1960s for classical computers that laid the foundations of modern programming and modeling languages for classical computers. Unfortunately, such a QC paradigm doesn’t exist. Thus, we foresee the need to build a novel QOP based on which future quantum programming and modeling languages shall be developed. Such QOP shall enable users with diverse backgrounds (e.g., computer scientists, software engineers, and physicists) to build QC applications cost-effectively, intuitively, and independently of low-level quantum mechanics characteristics (e.g., superposition and entanglement). This paper discusses the emerging work of QOP and presents research directions that the software engineering and programming communities can follow to build a successful QOP.
@InProceedings{QP4SE23p17,
author = {Shaukat Ali and Tao Yue},
title = {On the Need of Quantum-Oriented Paradigm},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {17--20},
doi = {10.1145/3617570.3617868},
year = {2023},
}
Publisher's Version
QP4SE '23: "Quantum Computing for Learning ..."
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies
Alessandro Pagano, Mario Angelelli, Miriana Calvano, Antonio Curci, and Antonio Piccinno
(University of Bari, Italy; University of Salento, Italy)
Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
@InProceedings{QP4SE23p13,
author = {Alessandro Pagano and Mario Angelelli and Miriana Calvano and Antonio Curci and Antonio Piccinno},
title = {Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {13--16},
doi = {10.1145/3617570.3617867},
year = {2023},
}
Publisher's Version
QP4SE '23: "Securing Smart Cities: Unraveling ..."
Securing Smart Cities: Unraveling Quantum as a Service
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Azzurra Ragone
(University of Bari, Italy)
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.
@InProceedings{QP4SE23p1,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Azzurra Ragone},
title = {Securing Smart Cities: Unraveling Quantum as a Service},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3617570.3617865},
year = {2023},
}
Publisher's Version
QP4SE '23: "Extending Developer Support: ..."
Extending Developer Support: Quantum Artificial Intelligence for Automotive Security
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Michele Scalera
(University of Bari, Italy)
With the adoption of advanced technology in the automotive field, managing the risks of attack in modern vehicles becomes essential. Some research works substantially exploit Machine Learning algorithms to identify threats conducted on vehicles, particularly on the Controller Area Network (CAN) bus. Therefore, it is necessary not only to use Intrusion Detection Systems (IDSs) to identify attacks but also to help the engineers in the automotive field understand the dangerousness of the attack and help them resolve the vulnerability. With the increasing attention to Quantum Computing (QC), QC-based Artificial Intelligence algorithms have become very popular among many researchers for improving the prediction and the time performance to identify an attack. This paper proposes a methodology, SeQuADE (Secure Quantum Automotive Development and Engineering), to identify CAN attacks and to support developers by proposing associated automotive vulnerabilities and solutions obtained from National Vulnerability Database (NVD).
@InProceedings{QP4SE23p7,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Michele Scalera},
title = {Extending Developer Support: Quantum Artificial Intelligence for Automotive Security},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3617570.3617866},
year = {2023},
}
Publisher's Version
QP4SE '23: "Quantum Computing for Learning ..."
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies
Alessandro Pagano, Mario Angelelli, Miriana Calvano, Antonio Curci, and Antonio Piccinno
(University of Bari, Italy; University of Salento, Italy)
Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
@InProceedings{QP4SE23p13,
author = {Alessandro Pagano and Mario Angelelli and Miriana Calvano and Antonio Curci and Antonio Piccinno},
title = {Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {13--16},
doi = {10.1145/3617570.3617867},
year = {2023},
}
Publisher's Version
QP4SE '23: "Quantum Computing for Learning ..."
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies
Alessandro Pagano, Mario Angelelli, Miriana Calvano, Antonio Curci, and Antonio Piccinno
(University of Bari, Italy; University of Salento, Italy)
Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
@InProceedings{QP4SE23p13,
author = {Alessandro Pagano and Mario Angelelli and Miriana Calvano and Antonio Curci and Antonio Piccinno},
title = {Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {13--16},
doi = {10.1145/3617570.3617867},
year = {2023},
}
Publisher's Version
QP4SE '23: "Securing Smart Cities: Unraveling ..."
Securing Smart Cities: Unraveling Quantum as a Service
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Azzurra Ragone
(University of Bari, Italy)
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.
@InProceedings{QP4SE23p1,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Azzurra Ragone},
title = {Securing Smart Cities: Unraveling Quantum as a Service},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3617570.3617865},
year = {2023},
}
Publisher's Version
QP4SE '23: "Extending Developer Support: ..."
Extending Developer Support: Quantum Artificial Intelligence for Automotive Security
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Michele Scalera
(University of Bari, Italy)
With the adoption of advanced technology in the automotive field, managing the risks of attack in modern vehicles becomes essential. Some research works substantially exploit Machine Learning algorithms to identify threats conducted on vehicles, particularly on the Controller Area Network (CAN) bus. Therefore, it is necessary not only to use Intrusion Detection Systems (IDSs) to identify attacks but also to help the engineers in the automotive field understand the dangerousness of the attack and help them resolve the vulnerability. With the increasing attention to Quantum Computing (QC), QC-based Artificial Intelligence algorithms have become very popular among many researchers for improving the prediction and the time performance to identify an attack. This paper proposes a methodology, SeQuADE (Secure Quantum Automotive Development and Engineering), to identify CAN attacks and to support developers by proposing associated automotive vulnerabilities and solutions obtained from National Vulnerability Database (NVD).
@InProceedings{QP4SE23p7,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Michele Scalera},
title = {Extending Developer Support: Quantum Artificial Intelligence for Automotive Security},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3617570.3617866},
year = {2023},
}
Publisher's Version
QP4SE '23: "Quantum Computing for Learning ..."
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies
Alessandro Pagano, Mario Angelelli, Miriana Calvano, Antonio Curci, and Antonio Piccinno
(University of Bari, Italy; University of Salento, Italy)
Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
@InProceedings{QP4SE23p13,
author = {Alessandro Pagano and Mario Angelelli and Miriana Calvano and Antonio Curci and Antonio Piccinno},
title = {Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {13--16},
doi = {10.1145/3617570.3617867},
year = {2023},
}
Publisher's Version
QP4SE '23: "Securing Smart Cities: Unraveling ..."
Securing Smart Cities: Unraveling Quantum as a Service
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Azzurra Ragone
(University of Bari, Italy)
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.
@InProceedings{QP4SE23p1,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Azzurra Ragone},
title = {Securing Smart Cities: Unraveling Quantum as a Service},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3617570.3617865},
year = {2023},
}
Publisher's Version
QP4SE '23: "Extending Developer Support: ..."
Extending Developer Support: Quantum Artificial Intelligence for Automotive Security
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Michele Scalera
(University of Bari, Italy)
With the adoption of advanced technology in the automotive field, managing the risks of attack in modern vehicles becomes essential. Some research works substantially exploit Machine Learning algorithms to identify threats conducted on vehicles, particularly on the Controller Area Network (CAN) bus. Therefore, it is necessary not only to use Intrusion Detection Systems (IDSs) to identify attacks but also to help the engineers in the automotive field understand the dangerousness of the attack and help them resolve the vulnerability. With the increasing attention to Quantum Computing (QC), QC-based Artificial Intelligence algorithms have become very popular among many researchers for improving the prediction and the time performance to identify an attack. This paper proposes a methodology, SeQuADE (Secure Quantum Automotive Development and Engineering), to identify CAN attacks and to support developers by proposing associated automotive vulnerabilities and solutions obtained from National Vulnerability Database (NVD).
@InProceedings{QP4SE23p7,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Michele Scalera},
title = {Extending Developer Support: Quantum Artificial Intelligence for Automotive Security},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3617570.3617866},
year = {2023},
}
Publisher's Version
QP4SE '23: "Quantum Computing for Learning ..."
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies
Alessandro Pagano, Mario Angelelli, Miriana Calvano, Antonio Curci, and Antonio Piccinno
(University of Bari, Italy; University of Salento, Italy)
Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
@InProceedings{QP4SE23p13,
author = {Alessandro Pagano and Mario Angelelli and Miriana Calvano and Antonio Curci and Antonio Piccinno},
title = {Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {13--16},
doi = {10.1145/3617570.3617867},
year = {2023},
}
Publisher's Version
QP4SE '23: "Securing Smart Cities: Unraveling ..."
Securing Smart Cities: Unraveling Quantum as a Service
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Azzurra Ragone
(University of Bari, Italy)
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.
@InProceedings{QP4SE23p1,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Azzurra Ragone},
title = {Securing Smart Cities: Unraveling Quantum as a Service},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {1--6},
doi = {10.1145/3617570.3617865},
year = {2023},
}
Publisher's Version
QP4SE '23: "Extending Developer Support: ..."
Extending Developer Support: Quantum Artificial Intelligence for Automotive Security
Danilo Caivano, Mirko De Vincentiis, Anibrata Pal, and Michele Scalera
(University of Bari, Italy)
With the adoption of advanced technology in the automotive field, managing the risks of attack in modern vehicles becomes essential. Some research works substantially exploit Machine Learning algorithms to identify threats conducted on vehicles, particularly on the Controller Area Network (CAN) bus. Therefore, it is necessary not only to use Intrusion Detection Systems (IDSs) to identify attacks but also to help the engineers in the automotive field understand the dangerousness of the attack and help them resolve the vulnerability. With the increasing attention to Quantum Computing (QC), QC-based Artificial Intelligence algorithms have become very popular among many researchers for improving the prediction and the time performance to identify an attack. This paper proposes a methodology, SeQuADE (Secure Quantum Automotive Development and Engineering), to identify CAN attacks and to support developers by proposing associated automotive vulnerabilities and solutions obtained from National Vulnerability Database (NVD).
@InProceedings{QP4SE23p7,
author = {Danilo Caivano and Mirko De Vincentiis and Anibrata Pal and Michele Scalera},
title = {Extending Developer Support: Quantum Artificial Intelligence for Automotive Security},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {7--12},
doi = {10.1145/3617570.3617866},
year = {2023},
}
Publisher's Version
QP4SE '23: "On the Need of Quantum-Oriented ..."
On the Need of Quantum-Oriented Paradigm
Shaukat Ali and Tao Yue
(Simula Research Laboratory, Norway; Oslo Metropolitan University, Norway)
Since the invention of Quantum Computing (QC) in the 1980s, substantial claims about QC’s ability to solve computational problems of unparalleled complexity have emerged. However, forty years later, no significant real-world QC applications exist. Indeed, the availability of small-scale noisy quantum computers is to blame. Still, simultaneously, the programming of quantum computers is too close to quantum hardware, requiring software engineers with specialized backgrounds to build QC applications and limiting the maximum exploitation of QC’s potential. Thus, there is a need for an abstract yet intuitive quantum-oriented paradigm (QOP) for building QC applications, similar to the object-oriented paradigm established in the 1960s for classical computers that laid the foundations of modern programming and modeling languages for classical computers. Unfortunately, such a QC paradigm doesn’t exist. Thus, we foresee the need to build a novel QOP based on which future quantum programming and modeling languages shall be developed. Such QOP shall enable users with diverse backgrounds (e.g., computer scientists, software engineers, and physicists) to build QC applications cost-effectively, intuitively, and independently of low-level quantum mechanics characteristics (e.g., superposition and entanglement). This paper discusses the emerging work of QOP and presents research directions that the software engineering and programming communities can follow to build a successful QOP.
@InProceedings{QP4SE23p17,
author = {Shaukat Ali and Tao Yue},
title = {On the Need of Quantum-Oriented Paradigm},
booktitle = {Proc.\ QP4SE},
publisher = {ACM},
pages = {17--20},
doi = {10.1145/3617570.3617868},
year = {2023},
}
Publisher's Version