The advent of quantum computing is poised to revolutionize computer science as we know it. The “quantum era” is just around the corner, promising to transform computation to the extent that solving NP-complete problems may become feasible. Major software companies, including IBM, Google, and others, are investing hundreds of billions of dollars to develop novel hardware and software tools to achieve quantum supremacy and embrace this paradigm shift. However, large-scale pure quantum software systems remain a distant goal, creating growing interest in the development of hybrid Quantum-Based Software Systems (QBSs), where quantum components are integrated into traditional software applications. This emerging landscape necessitates the establishment of design principles, quality assurance frameworks, and verification and validation practices for Quantum Programming, forming the Software Engineering for Quantum Programming (SE4QP) research domain. At the same time, it is equally crucial to explore the complementary perspective: Quantum Programming for Software Engineering (QP4SE). Many Software Engineering challenges demand significant computational resources. Tasks such as the automatic generation and optimization of test suites or the application of artificial intelligence techniques to evaluate and improve software quality often require substantial effort to complete within a reasonable timeframe. Recently, quantum technologies have demonstrated their potential to address problems that are computationally infeasible with traditional approaches. In the past few years, numerous quantum based solutions have emerged. Quantum optimization algorithms have been proposed as more efficient alternatives to conventional optimization techniques. Similarly, the rise of quantum machine learning has garnered attention from researchers and practitioners, offering quantum-based counterparts to traditional ML algorithms, such as Quantum Support Vector Machines (QSVM). In this context, researchers have begun applying quantum-based approaches to Software Engineering problems to enhance the performance of existing solutions. Quantum programming, therefore, holds great promise for solving domain-specific challenges in Software Engineering or improving current methods’ efficiency. This highlights the need for researchers and practitioners to devise and evaluate new methodologies for applying Quantum Programming to Software Engineering. To address these needs, Quantum Programming for Software Engineering (QP4SE) has served as a forum for researchers and practitioners to present and discuss the latest challenges in this area while sharing best practices for developing innovative solutions.
Preface for “Quantum Programming for Software Engineering (QP4SE)”
Barletta V. S.
;
2024-01-01
Abstract
The advent of quantum computing is poised to revolutionize computer science as we know it. The “quantum era” is just around the corner, promising to transform computation to the extent that solving NP-complete problems may become feasible. Major software companies, including IBM, Google, and others, are investing hundreds of billions of dollars to develop novel hardware and software tools to achieve quantum supremacy and embrace this paradigm shift. However, large-scale pure quantum software systems remain a distant goal, creating growing interest in the development of hybrid Quantum-Based Software Systems (QBSs), where quantum components are integrated into traditional software applications. This emerging landscape necessitates the establishment of design principles, quality assurance frameworks, and verification and validation practices for Quantum Programming, forming the Software Engineering for Quantum Programming (SE4QP) research domain. At the same time, it is equally crucial to explore the complementary perspective: Quantum Programming for Software Engineering (QP4SE). Many Software Engineering challenges demand significant computational resources. Tasks such as the automatic generation and optimization of test suites or the application of artificial intelligence techniques to evaluate and improve software quality often require substantial effort to complete within a reasonable timeframe. Recently, quantum technologies have demonstrated their potential to address problems that are computationally infeasible with traditional approaches. In the past few years, numerous quantum based solutions have emerged. Quantum optimization algorithms have been proposed as more efficient alternatives to conventional optimization techniques. Similarly, the rise of quantum machine learning has garnered attention from researchers and practitioners, offering quantum-based counterparts to traditional ML algorithms, such as Quantum Support Vector Machines (QSVM). In this context, researchers have begun applying quantum-based approaches to Software Engineering problems to enhance the performance of existing solutions. Quantum programming, therefore, holds great promise for solving domain-specific challenges in Software Engineering or improving current methods’ efficiency. This highlights the need for researchers and practitioners to devise and evaluate new methodologies for applying Quantum Programming to Software Engineering. To address these needs, Quantum Programming for Software Engineering (QP4SE) has served as a forum for researchers and practitioners to present and discuss the latest challenges in this area while sharing best practices for developing innovative solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
