Contemporary Quantum Computing Use Cases: Taxonomy, Review and Challenges

Singh, Jaiteg; Bhangu, Kamalpreet Singh

doi:10.1007/s11831-022-09809-5

Cited by 28 publications

(11 citation statements)

References 184 publications

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“…The quantum pioneers could achieve an advantage that is orders of magnitude superior to their classical counterparts resulting in vastly superior computation (optimization, search, and cryptography), communication, and measurement [23]. Consequently, both innovators and regulators [23,[60][61][62] need to anticipate potentially malicious use of QT by (1) utilizing both technology forecasting [63] and horizon scanning techniques [64] anticipating novel dual use quantum use cases [12,22] and identify novel use cases that are currently out of sight, in unison with (2) implementing appropriate safeguards at the initial stages of the TRL [2]. Such assessments should be conducted by governments and at the organizational level and linked to certification by independent notified bodies in connection with market introduction.…”

Section: Dual Use: Proactively Anticipate the Malicious Use Of Quantu...mentioning

confidence: 99%

“…Principle 8 seeks to stimulate sustainable, cross-disciplinary innovation resulting in positive externalities, spillover effects [96], and a virtuous cycle of progress comparable to that of the semiconductor industry 12 . QT are part of a larger sociotechnical ecosystem, as it relies on other technologies and research fields that QT, in its turn, can enable or enhance.…”

Section: Complementary Innovation: Actively Stimulate Sustainable Cro...mentioning

confidence: 99%

“…For a catalog of nine principles relating to quantum computing, see[41]. For ten broader principles and a list of ten risks pertaining to the entire suite of QT, see[39] 12. A virtuous cycle, or positive feedback loop, requires ongoing private and public investments, research, development and engineering efforts, advancing QT (including successful commercial QT applications) and attracting talent[112].…”

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confidence: 99%

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Ten principles for responsible quantum innovation

Kop,

Aboy,

De Jong

et al. 2024

Quantum Sci. Technol.

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This paper proposes a set of guiding principles for responsible quantum innovation. The principles are organized into three functional categories: safeguarding, engaging, and advancing (SEA), and are linked to central values in responsible research and innovation (RRI). Utilizing a global equity normative framework and literature-based methodology, we connect the quantum-SEA categories to promise and perils specific to quantum technology (QT). The paper operationalizes the responsible QT framework by proposing ten actionable principles to help address the risks, challenges, and opportunities associated with the entire suite of second-generation QTs, which includes the quantum computing, sensing, simulation, and networking domains. Each quantum domain has different technology readiness levels, risks, and affordances, with sensing and simulation arguably being closest to market entrance. Our proposal aims to catalyze a much-needed interdisciplinary effort within the quantum community to establish a foundation of quantum-specific and quantum-tailored principles for responsible quantum innovation. The overarching objective of this interdisciplinary effort is to steer the development and use of QT in a direction not only consistent with a values-based society but also a direction that contributes to addressing some of society’s most pressing needs and goals.

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Section: Dual Use: Proactively Anticipate the Malicious Use Of Quantu...mentioning

confidence: 99%

Section: Complementary Innovation: Actively Stimulate Sustainable Cro...mentioning

confidence: 99%

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confidence: 99%

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Ten principles for responsible quantum innovation

Kop,

Aboy,

De Jong

et al. 2024

Quantum Sci. Technol.

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“…Furthermore, Applications of Data Science in Quantum Computing [1,21,24] are explored, with a dedicated focus on Quantum Cryptography and its secure data communication applications, as well as Quantum-enhanced Data Analysis [12,18,20,23], showcasing case studies and examples. Finally, the paper concludes with a section on Future Directions and Challenges, discussing emerging trends, open research questions, and challenges in the field of data science for quantum computing.…”

Section: Introductionmentioning

confidence: 99%

Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing

Khan

2024

Preprint

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Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.

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“…In [15], the authors suggest bridging the gap between computer professionals and non-physicists by offering the conceptual and notational information that segregates quantum computing from conventional computing. In [16], the authors stated that interest in building dedicated quantum information science and engineering (QISE) education programs have greatly expanded in recent years and that there will be a need for a wide variety of expertise and education levels to create a balanced technical workforce like that seen in other professional scientific and engineering fields.…”

mentioning

confidence: 99%

The Foundations of Quantum Computing and Their Relation to Software Engineering

Juárez-Ramírez,

Navarro-Cota,

Jiménez

et al. 2024

Proceedings of ISP RAS

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The principles of quantum mechanics – superposition, entanglement, measurement, and decoherence – form the foundation of quantum computing. Qubits, which are abstract objects having a mathematical expression to implement the rules of quantum physics, are the fundamental building blocks of computation. Software is a key component of quantum computing, along with quantum hardware. Algorithms make up software, and they are implemented using logic gates and quantum circuits. These qualities make quantum computing a paradigm that non-physicists find difficult to comprehend. It is crucial to incorporate a conceptual framework of the principles upon which quantum computing is founded into this new method of creating software. In this paper, we present a kind of taxonomical view of the fundamental concepts of quantum computing and the derived concepts that integrate the emerging discipline of quantum software engineering. Because the systematic review's main goal is to identify the core ideas behind quantum computing and quantum software, we conducted a quasi-systematic mapping as part of the review process. The findings can serve as a starting point for computer science teachers and students to address the study of this field.

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Contemporary Quantum Computing Use Cases: Taxonomy, Review and Challenges

Cited by 28 publications

References 184 publications

Ten principles for responsible quantum innovation

Ten principles for responsible quantum innovation

Quantum Data Science: Leveraging Data Analytics for Advancing Quantum Computing

The Foundations of Quantum Computing and Their Relation to Software Engineering

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