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Advances in Engineering Software

Engineering the development of quantum programs: Application to the Boolean satisfiability problem

Diego Alonso, Pedro Sánchez, Francisco Sánchez-Rubio

Published Aug 11, 2022• 21 citations
Publisher

Abstract

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.

DOI
10.1016/j.advengsoft.2022.103216

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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.
Several researchers have recently highlighted the convenience of adopting and adapting the techniques and tools of Software Engineering for the development of programs in the eld of Quantum Computing. Model-driven engineering is one of the techniques that has generated the best results in the eld of Software Engineering in the last twenty years, so it seems sensible to explore the benet of applying this technique in the eld of quantum programming, given the conceptual dierences that exist between quantum and classical programming primitives. In this context, the Boolean satisability problem has received attention as quantum programming provides a solution that reduces to polynomial the search for solutions to the problem. This work addresses the consideration of applying model-driven engineering to ease the generation of quantum programs and make it more aordable. To demonstrate the feasibility of the approach, we apply it to solve the Boolean satisability problem by taking advantage of the benets of model compilation, in terms of obtaining a quantum circuit that minimizes the number of gates involved in the solution, thus limiting the possible de-coherence of the quantum system.
ABSTRACT Background Quantum Computing (QC) represents a disruptive paradigm in computing, with significant implications across various domains such as medicine, logistics, chemistry, and defense. However, quantum software development—the discipline that enables the exploitation of QC's potential—faces considerable challenges due to the inherent complexity of quantum mechanics. Objective This work aims to facilitate the systematic development of quantum software by introducing a method aligned with classical software engineering principles, particularly the analysis and synthesis phases, through the automated generation and optimization of quantum circuit components. Methods The proposed approach focuses on the automated generation of quantum circuit components whose behavior can be formally described using truth tables. To this end, (i) a set of algorithms for the automatic generation of components is introduced, (ii) an optimization algorithm is developed to improve the efficiency of the generated components, and (iii) an automated support system is provided, allowing users to specify truth tables through multiple modeling strategies. Results The resulting system enables a structured and automated process for building reusable quantum circuit components, reducing manual effort and supporting higher abstraction levels in quantum programming. The design of the system adheres to foundational principles of Software Engineering, including agnosticism and automatic code generation. Conclusions This proposal contributes to quantum software development by providing a practical, engineering‐driven methodology for generating and optimizing quantum circuit components, thereby improving the efficiency, scalability, and accessibility of quantum programming practices.
A crucial task for the systematic application of model-driven engineering techniques in the development of quantum software is the definition of metamodels, as a first step towards automatic code generation and integration with other tools. The importance is even greater when considering recent work where the first extensions to UML for modelling quantum circuits are emerging and the characterisation of these extensions in terms of their suitability for a model-driven approach becomes unavoidable. After reviewing the related work, this article proposes a unified metamodel for modelling quantum circuits, together with five strategies for its use and some examples of its application. The article also provides a set of constraints for using the identified strategies, a set of procedures for transforming the models between the strategies, and an analysis of the suitability of each strategy for performing common tasks in a model-driven quantum software development environment. All of these resources will enable the quantum software community to speak the same language and use the same set of abstractions, which are key to furthering the development of tools to be built as part of future model-driven quantum software development frameworks.
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.
This study explores how well Grover's Algorithm performs in solving the Boolean Satisfiability Problem (SAT) using quantum circuits. The algorithm is implemented with IBM's Qiskit framework and compared to classical brute-force methods. Experiments focus on 3-SAT, 4-SAT, and 5-SAT problems, using quantum simulators and IBM quantum hardware. The results show that Grover's Algorithm is more efficient, offering a theoretical quadratic speedup over classical methods. However, practical issues like limited qubit availability, hardware noise, and optimization challenges impact its current performance. The data highlights the potential for quantum computing to scale and solve NP-complete problems. This research shows how quantum computing can improve problem-solving and lays the groundwork for future studies on more complex SAT problems and advanced quantum hardware.
This study explores how well Grover's Algorithm performs in solving the Boolean Satisfiability Problem (SAT) using quantum circuits. The algorithm is implemented with IBM's Qiskit framework and compared to classical brute-force methods. Experiments focus on 3-SAT, 4-SAT, and 5-SAT problems, using quantum simulators and IBM quantum hardware. The results show that Grover's Algorithm is more efficient, offering a theoretical quadratic speedup over classical methods. However, practical issues like limited qubit availability, hardware noise, and optimization challenges impact its current performance. The data highlights the potential for quantum computing to scale and solve NP-complete problems. This research shows how quantum computing can improve problem-solving and lays the groundwork for future studies on more complex SAT problems and advanced quantum hardware.