Speakers

Shaukat Ali

Title

Quantum Artificial Intelligence for Classical Software Engineering

Abstract

Quantum Artificial Intelligence (QAI) aims to enhance classical AI algorithms and develop entirely new ones, providing benefits such as faster training, efficient optimization, and improved prediction. Although QAI is an emerging field, recent studies have demonstrated its advantages across various domains. Along the same lines, this talk will present the applications of QAI in classical software engineering. Specifically, it will discuss our efforts to apply quantum optimization algorithms (i.e., quantum annealing and quantum approximate optimization algorithms) to optimize test cases for classical software systems. The talk will also present our research on using quantum machine learning for software regression testing in real-world applications. Finally, it will cover the positive results obtained, current limitations, and future research challenges that must be addressed to enable the broader adoption of QAI.

Biography

Shaukat Ali is the Head of the Department, Chief Research Scientist, and Research Professor at Simula Research Laboratory in Oslo, Norway. His research focuses on developing innovative techniques for verifying and validating classical and quantum software systems. Recently, he has been focusing on the emerging field of quantum software engineering, which includes quantum software development, testing, debugging, and repair. Moreover, his recent work has been key in establishing the foundations of quantum artificial intelligence and its real-world applications.

Jianjun Zhao

Title

Quantum Optimization Algorithms Meet Software Engineering

Abstract

Quantum optimization algorithms offer new possibilities for addressing complex combinatorial problems. Recently, there has been growing interest in exploring how these algorithms might be applied to classical software engineering tasks, such as test case selection and regression testing. In this talk, I will provide an overview of emerging research in this area, summarizing initial findings and approaches. I will also discuss why this research direction deserves further attention and what opportunities it may open for future research.

Biography

Jianjun Zhao is a Professor at Kyushu University, Japan. His research focuses on classical and quantum software engineering, with an emphasis on improving software quality and reliability. He has published over 150 research papers in leading international conferences and journals, and is actively engaged in interdisciplinary research that bridges software engineering and quantum computing. He also contributes to shaping the emerging field of quantum software engineering through international collaboration and community efforts.

Eneko Osaba Icedo

Title

Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms

Abstract

This paper presents a novel hybrid approach to solving real-world drone routing problems by leveraging the capabilities of quantum computing. The proposed method, coined Quantum for Drone Routing (Q4DR), integrates the two most prominent paradigms in the field: quantum gate-based computing, through the Eclipse Qrisp programming language; and quantum annealers, by means of D-Wave System's devices. The algorithm is divided into two different phases: an initial clustering phase executed using a Quantum Approximate Optimization Algorithm (QAOA), and a routing phase employing quantum annealers. The efficacy of Q4DR is demonstrated through three use cases of increasing complexity, each incorporating real-world constraints such as asymmetric costs, forbidden paths, and itinerant charging points. This research contributes to the growing body of work in quantum optimization, showcasing the practical applications of quantum computing in logistics and route planning.

Biography

Dr. Eneko Osaba works at TECNALIA as principal researcher in the DIGITAL/Next area. He obtained his Ph.D. degree on Artificial Intelligence in 2015. He has participated in more than 30 research projects. He has contributed in the development of more than 180 papers, including more than 32 Q1. He has performed several stays in universities of United Kingdom, Italy and Malta. He has served as a member of the program and/or organizing committee in more than 60 international conferences. He is member of the editorial board of Data in Brief and Journal of Advanced Transportation. He has acted as guest editor in journals such as Neurocomputing, Journal of Supercomputing, Swarm and Evolutionary Computation and IEEE ITS Magazine. Finally, in 2022, Eneko was recognized by the Basque Research and Technology Alliance as one of the most promising young researchers of the Basque Country, Spain. Also, Eneko is part of the Stanford/Elsevier's World's Top 2% Scientist List.

Liqiang Lu

Title

Choco-Q: Commute Hamiltonian-based QAOA for Constrained Binary Optimization

Abstract

Constrained binary optimization aims to find an optimal assignment to minimize or maximize the objective meanwhile satisfying the constraints, which is a representative NP problem in various domains, including transportation, scheduling, and economy. Quantum approximate optimization algorithms (QAOA) provide a promising methodology for solving this problem by exploiting the parallelism of quantum entanglement. However, existing QAOA approaches based on penalty-term or Hamiltonian simulation fail to thoroughly encode the constraints, leading to extremely low success rate and long searching latency.
This paper proposes Choco-Q, a formal and universal framework for constrained binary optimization problems, which comprehensively covers all constraints and exhibits high deployability for current quantum devices. The main innovation of Choco-Q is to embed the commute Hamiltonian as the driver Hamiltonian, resulting in a much more general encoding formulation that can deal with arbitrary linear constraints. Leveraging the arithmetic features of commute Hamiltonian, we propose three optimization techniques to squeeze the overall circuit complexity, including Hamiltonian serialization, equivalent decomposition, and variable elimination. The serialization mechanism transforms the original Hamiltonian into smaller ones. Our decomposition methods only take linear time complexity, achieving end-to-end acceleration. Experiments demonstrate that Choco-Q shows more than 235× algorithmic improvement in successfully finding the optimal solution, and achieves 4.69× end-to-end acceleration, compared to prior QAOA designs.

Biography

Liqiang Lu is a ZJU100 Young Professor in the College of Computer Science, Zhejiang University (ZJU), China. His research interests include quantum computing, computer architecture, deep learning accelerator, and software-hardware codesign. He has authored more than 30 scientific publications in premier international journals and conferences in related domains, including ISCA, MICRO, HPCA, ASPLOS, FCCM, DAC, IEEE Micro, and TCAD. He also serves as a TPC member in the premier conferences in the related domain, including MICRO, DAC, ICCAD, FPT, HPCC, etc.

Yuechen Li

Title

Exploring Quantum Optimization for Software Engineering

Abstract

Owing to the inherent merit of quantum parallelism, quantum computing is promising to cope with complex discrete optimization problems more efficiently than classical computing. Even in the noisy intermediate-scale quantum era, quantum hardware has been employed for real-world optimization tasks and preliminarily demonstrated its applicability in practice. In software engineering, there usually exists a tradeoff between effectiveness and costs for the software development life cycle. In view of optimization, valid solutions should be sought to follow the requirements and balance the two mentioned items. However, while searching for solutions, a great deal of data may be involved and various requirements could give practical constraints. This complexity brings the opportunity to leverage the advantages of quantum computing for optimization problems in software engineering and also promote the real-world application of the existing quantum hardware. Hence, this talk, including three main parts, aims to link quantum computing with software engineering, and discusses the landscape in both academia and industry. Considering the quantum hardware available to researchers, this talk first introduces several paradigms and models of quantum optimization, which are possibly suitable for involved issues in software engineering. Then, this talk summarizes the state-of-the-art based on recent publications within the scope, where this part further interprets feasible models for quantum optimization and specific tasks in software engineering. Finally, this talk concentrates on two general models to outline possible future directions and offer reasonable research steps.

Biography

Yuechen Li is a Ph.D candidate at Beihang University, Beijing. His current research interest is quantum software engineering, particularly focusing on quantum software testing and quantum optimization algorithms.

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