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.
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.
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 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.
Quantum computing is poised to revolutionize computational paradigms by leveraging quantum mechanics principles such as superposition and entanglement.However, the full-scale deployment of quantum applications remains constrained by hardware limitations, including high error rates and quantum decoherence.Quantum Software Engineering (QSE) emerges as a critical field addressing these challenges by optimizing algorithm design, error mitigation, and compiler strategies to enhance fault tolerance.Algorithm design in QSE focuses on developing quantum algorithms that efficiently exploit quantum parallelism while minimizing resource overhead.Key advancements include quantum variational algorithms, hybrid quantum-classical frameworks, and novel quantum heuristics tailored for optimization and cryptographic problems.Error mitigation techniques play a pivotal role in extending quantum circuit reliability without requiring full quantum error correction.Methods such as zero-noise extrapolation, probabilistic error cancellation, and quantum embedding techniques help reduce computational inaccuracies.Additionally, compiler optimization ensures efficient quantum program execution by minimizing gate depth, optimizing qubit mapping, and leveraging noise-adaptive scheduling to enhance quantum hardware performance.This paper explores the synergy between these three pillars of QSE, analyzing their impact on improving the feasibility of fault-tolerant quantum computing.It also examines emerging trends, including AI-driven quantum compilers, adaptive error mitigation techniques, and hardware-aware quantum software development.By bridging the gap between theoretical advancements and practical implementations, QSE provides a structured approach to accelerating quantum computing adoption across domains such as cryptography, materials science, and artificial intelligence.The findings underscore the necessity of interdisciplinary collaboration in developing robust quantum software solutions that maximize computational efficiency while mitigating inherent quantum hardware limitations.
Abstract As bigger quantum processors with hundreds of qubits become increasingly available, the potential for quantum computing to solve problems intractable for classical computers is becoming more tangible. Designing efficient quantum algorithms and software in tandem is key to achieving quantum advantage. Quantum software engineering is challenging due to the unique counterintuitive nature of quantum logic. Moreover, with larger quantum systems, traditional programming using quantum assembly language and qubit-level reasoning is becoming infeasible. Automated Quantum Software Engineering (AQSE) can help to reduce the barrier to entry, speed up development, reduce errors, and improve the efficiency of quantum software. This article elucidates the motivation to research AQSE (why), a precise description of such a framework (what), and reflections on components that are required for implementing it (how).
We present a new software package for efficient quantum circuit generation, designed to achieve optimal runtime performance. Despite being in an early stage of development, our implementation demonstrates significant advantages over existing tools. Using the quantum Fourier transform (QFT) as a benchmark, we show that our backend can generate circuits for systems with up to 2000 qubits faster than widely used frameworks such as Qiskit and Q#. This improvement is particularly relevant for applications where classical preprocessing time, including circuit generation, must be minimized to not diminish any potential quantum advantage - for example, in combinatorial optimization tasks. Additionally, our software provides high-level primitives for bit- and integer-level manipulations, offering a simplified interface for integration with high-level quantum programming languages.
As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE.