Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Software Practice and Experience

Generation of Quantum Software From Truth Tables

Macario Polo, Ignacio García‐Rodríguez de Guzmán, Manuel Serrano, Mario G. Piattini

Published Apr 30, 2025• 2 citations
Publisher

Abstract

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.

DOI
10.1002/spe.3426

Related papers

Generating Quantum Software from Truth Tables

Macario Polo Usaola, Ignacio García‐Rodríguez de Guzmán, Manuel Serrano +1 more

Quantum computing promises significant advancements over classical computing by enabling exponential speedups for specific problems. Despite its potential, quantum software development remains challenging due to its complexity, cost, and susceptibility to errors. Aligned with the novel paradigm of Quantum Software Engineering (QSE), this paper introduces a tool designed to automate the generation of quantum software components from truth tables. The tool simplifies the development process by allowing users to specify expected output values for given inputs, which the tool then translates into the corresponding quantum code. This approach reduces the manual effort required and helps ensure accuracy in quantum program development. Our method not only streamlines quantum software generation but also optimizes the resulting quantum circuits by minimizing unnecessary computations. The paper provides a detailed overview of the algorithm, tool implementation, and potential applications, highlighting the tool's efficacy in facilitating more efficient quantum software development.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•2 citations

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

Diego Alonso, Pedro Sánchez, Francisco Sánchez-Rubio

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.

Advances in Engineering Software•Aug 11, 2022•21 citations

基于拓扑的量子计算的自适应量子电路设计

Jincheng Zhang

Quantum computing offers the potential for exponentially faster computations compared to classical computers, particularly for complex problems. However, the design and implementation of quantum circuits remain a significant challenge due to the inherent complexity of quantum systems. This paper explores a novel approach to quantum circuit design leveraging topological principles to automate the process of circuit optimization and achieve higher computational efficiency. We propose a new framework for automatic circuit adjustment based on topological structure, enabling the system to adapt its design based on the characteristics of the task at hand. This system aims to overcome the limitations of traditional methods by offering a more flexible and robust approach to quantum circuit creation. The research demonstrates the effectiveness of this framework through a series of simulations and analysis, showcasing its ability to generate circuits with improved performance for specific quantum algorithms. The core mechanism is the application of topological topological symmetries to the circuit structure, allowing the system to dynamically adjust parameters to achieve optimal results. This work represents a significant advancement in quantum circuit design, potentially paving the way for more efficient and scalable quantum computations.

•Sep 4, 2026•0 citations

Dynamic Quantum Circuit Synthesis and Optimization

Jincheng Zhang

This paper presents a novel approach to quantum circuit synthesis and optimization, addressing the significant challenges in designing efficient quantum algorithms. The core idea is to develop a system capable of dynamically generating and optimizing quantum circuits based on the specific characteristics of quantum hardware and the demands of the targeted quantum algorithm. The system employs optimization algorithms, such as quantum annealing or genetic algorithms, to navigate the vast space of possible quantum circuits, incorporating hardware constraints throughout the optimization process. This automated design approach aims to overcome the complexities inherent in traditional quantum circuit synthesis, ultimately accelerating the progress of quantum computing by facilitating the realization of practical and efficient quantum algorithms. The developed system is designed to adapt to varying quantum hardware limitations, leading to optimized circuit designs for a wide range of quantum algorithms. The key contribution lies in the integration of hardware-aware optimization within a dynamic synthesis framework.

•Aug 31, 2026•0 citations

Design Automation of Quantum Circuits for Engineering Systems: Gate Optimization and Noise-Aware Compilation in NISQ-Aware Hardware

Erim Engin

The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.

Next generation.•Nov 13, 2025•0 citations

An approach for automated generation of quantum computing models using deep learning

Níyazí Furkan Bar, Mehmet Karaköse

Quantum computing promises remarkable computational power with minimal energy consumption. However, the complexity of developing quantum circuits and codes hinders fully exploiting this potential. The study proposes an approach based on the automatic quantum circuit and code generation based on deep learning. It enables the resynthesis of existing circuits and the creation of new ones from undefined inputs. The system transforms inputs into reversible truth tables, generates a quantum unitary matrix, corrects errors, optimizes it, and converts it into a quantum code or circuit. This approach has been implemented on circuits and codes that involve up to five variables. Rigorous evaluations include both the Deep Neural Network and the overall approach. Although the DNN output does not guarantee absolute correctness, our approach compensates with supplementary processes, ensuring the precise generation of quantum codes and circuits. Comprehensive testing confirmed the approach's effectiveness in overcoming challenges in quantum circuit and code development. • Benefiting from the low energy and high computing power potential offered by quantum computing. • Developing quantum circuits and algorithms is a challenging task. • The number of competent people in the field who can develop quantum circuits and algorithms is low. • Classical algorithms do not work on quantum computers. • Insufficient number of quantum circuits and algorithms running on quantum computers.

Ain Shams Engineering Journal•Feb 28, 2025•8 citations
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.
Quantum computing promises significant advancements over classical computing by enabling exponential speedups for specific problems. Despite its potential, quantum software development remains challenging due to its complexity, cost, and susceptibility to errors. Aligned with the novel paradigm of Quantum Software Engineering (QSE), this paper introduces a tool designed to automate the generation of quantum software components from truth tables. The tool simplifies the development process by allowing users to specify expected output values for given inputs, which the tool then translates into the corresponding quantum code. This approach reduces the manual effort required and helps ensure accuracy in quantum program development. Our method not only streamlines quantum software generation but also optimizes the resulting quantum circuits by minimizing unnecessary computations. The paper provides a detailed overview of the algorithm, tool implementation, and potential applications, highlighting the tool's efficacy in facilitating more efficient quantum software development.
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.
Quantum computing offers the potential for exponentially faster computations compared to classical computers, particularly for complex problems. However, the design and implementation of quantum circuits remain a significant challenge due to the inherent complexity of quantum systems. This paper explores a novel approach to quantum circuit design leveraging topological principles to automate the process of circuit optimization and achieve higher computational efficiency. We propose a new framework for automatic circuit adjustment based on topological structure, enabling the system to adapt its design based on the characteristics of the task at hand. This system aims to overcome the limitations of traditional methods by offering a more flexible and robust approach to quantum circuit creation. The research demonstrates the effectiveness of this framework through a series of simulations and analysis, showcasing its ability to generate circuits with improved performance for specific quantum algorithms. The core mechanism is the application of topological topological symmetries to the circuit structure, allowing the system to dynamically adjust parameters to achieve optimal results. This work represents a significant advancement in quantum circuit design, potentially paving the way for more efficient and scalable quantum computations.
This paper presents a novel approach to quantum circuit synthesis and optimization, addressing the significant challenges in designing efficient quantum algorithms. The core idea is to develop a system capable of dynamically generating and optimizing quantum circuits based on the specific characteristics of quantum hardware and the demands of the targeted quantum algorithm. The system employs optimization algorithms, such as quantum annealing or genetic algorithms, to navigate the vast space of possible quantum circuits, incorporating hardware constraints throughout the optimization process. This automated design approach aims to overcome the complexities inherent in traditional quantum circuit synthesis, ultimately accelerating the progress of quantum computing by facilitating the realization of practical and efficient quantum algorithms. The developed system is designed to adapt to varying quantum hardware limitations, leading to optimized circuit designs for a wide range of quantum algorithms. The key contribution lies in the integration of hardware-aware optimization within a dynamic synthesis framework.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
Quantum computing promises remarkable computational power with minimal energy consumption. However, the complexity of developing quantum circuits and codes hinders fully exploiting this potential. The study proposes an approach based on the automatic quantum circuit and code generation based on deep learning. It enables the resynthesis of existing circuits and the creation of new ones from undefined inputs. The system transforms inputs into reversible truth tables, generates a quantum unitary matrix, corrects errors, optimizes it, and converts it into a quantum code or circuit. This approach has been implemented on circuits and codes that involve up to five variables. Rigorous evaluations include both the Deep Neural Network and the overall approach. Although the DNN output does not guarantee absolute correctness, our approach compensates with supplementary processes, ensuring the precise generation of quantum codes and circuits. Comprehensive testing confirmed the approach's effectiveness in overcoming challenges in quantum circuit and code development. • Benefiting from the low energy and high computing power potential offered by quantum computing. • Developing quantum circuits and algorithms is a challenging task. • The number of competent people in the field who can develop quantum circuits and algorithms is low. • Classical algorithms do not work on quantum computers. • Insufficient number of quantum circuits and algorithms running on quantum computers.