Quantum technologies are emerging. Dedicated languages for programming Quantum machines are emerging as well and already used in different settings. Orthogonal to this development, Model-Driven Engineering (MDE) is explored to ease the development of software systems by applying modeling techniques such as Domain-Specific Modeling Languages and generative techniques such as code generation. In this position paper, we argue for a dedicated research line which deals with the exploration of how MDE may be applied for Quantum technologies. This combination would allow to speed- up the development of Quantum software, open the door for domain experts to utilize Quantum technologies, and may provide an additional abstraction layer over existing Quantum hardware architectures and programming languages. We outline several research challenges which we derived from a demonstration case of how to exploit domain-specific modeling for social network analysis on Quantum technologies.
Quantum technologies are emerging. Dedicated languages for programming Quantum machines are emerging as well and already used in different settings. Orthogonal to this development, Model-Driven Engineering (MDE) is explored to ease the development of software systems by applying modeling techniques such as Domain-Specific Modeling Languages and generative techniques such as code generation. In this position paper, we argue for a dedicated research line which deals with the exploration of how MDE may be applied for Quantum technologies. This combination would allow to speed- up the development of Quantum software, open the door for domain experts to utilize Quantum technologies, and may provide an additional abstraction layer over existing Quantum hardware architectures and programming languages. We outline several research challenges which we derived from a demonstration case of how to exploit domain-specific modeling for social network analysis on Quantum technologies.
The development of quantum programs is becoming a reality due to the rapid advancement of quantum computing. Over the past few years, a multitude of hardware platforms, algorithms, and programming languages have emerged to support this paradigm. By the very nature of Quantum Mechanics principles, there is an enormous change of philosophy when building quantum programs, which operate in a probabilistic space, unlike the deterministic behaviour shown by classical programming languages. These conceptual differences can be overcome by using techniques and tools of Software Engineering. In this paper, we apply Model-Driven Engineering techniques in a systematic way to ease the generation of quantum programs and we apply it to solve the satisfiability problem, very important in many engineering domains like verification of discrete systems and test of integrated circuits. To that aim, we contribute with a metamodel for representing quantum circuits and a model-to-text transformation to generate working IBM Qiskit code. This model-driven infrastructure is employed to automatically generate quantum programs from SAT equations through a model-to-model transformation that embeds Grover’s algorithm. Besides, we provide formulas for calculating the number of required quantum elements from SAT equations, crucial in the current context of limited quantum resources. The interoperability with other tools and the extensibility to target additional quantum platforms is guaranteed thanks to the use of a model-based toolchain. We cover several usage scenarios to validate the approach, providing exemplary SAT equations, the generated Qiskit code and the results of executing this code in IBM Quantum infrastructure.
Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications.
In the realm of classical software engineering, model-driven optimization has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimization can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist—e.g., based on reinforcement learning and genetic programming—these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimization approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known Quantum algorithm.
Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study.
Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains.
In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.
Over the past decade, Artificial Intelligence (AI) has provided enormous new possibilities and opportunities, but also new demands and requirements for software systems. In particular, Machine Learning (ML) has proven useful in almost every vertical application domain. In the decade ahead, an unprecedented paradigm shift from classical computing towards Quantum Computing (QC), with perhaps a quantum-classical hybrid model, is expected. We argue that the Model-Driven Engineering (MDE) paradigm can be an enabler and a facilitator, when it comes to the quantum and the quantum-classical hybrid applications. This includes not only automated code generation, but also automated model checking and verification, as well as model analysis in the early design phases, and model-to-model transformations both at the design-time and at the runtime. In this paper, the vision is focused on MDE for Quantum AI, particularly Quantum ML for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) applications.
Abstract Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing’s fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain.