Quantum software is often described using circuit-level metrics, including the number of qubits, the number of gates, circuit depth, the number of two-qubit gates, and the number of measurement operations. These metrics are useful for estimating program size and execution cost, but they do not directly describe whether and to what extent qubits become structurally related through possible entanglement during program execution. This paper argues that entanglement dependence provides a basis for measuring a quantum-specific form of software complexity. The proposed metrics capture possible entanglement-related structural dependence, not physical entanglement observed in a particular execution. Based on this notion, we define a focused set of six metrics that characterize the amount, density, local connectivity, global scope, program span, and modular crossing of entanglement-related dependencies. We also analyze these metrics using Weyuker’s properties for evaluating software complexity measures. The paper presents the metrics as a measurement framework and research direction for empirical quantum software engineering. It discusses how future studies can validate the metrics and examine their usefulness for testing, debugging, maintenance, and program comprehension of quantum software.
Quantum software is often described using circuit-level metrics, including the number of qubits, the number of gates, circuit depth, the number of two-qubit gates, and the number of measurement operations. These metrics are useful for estimating program size and execution cost, but they do not directly describe whether and to what extent qubits become structurally related through possible entanglement during program execution. This paper argues that entanglement dependence provides a basis for measuring a quantum-specific form of software complexity. The proposed metrics capture possible entanglement-related structural dependence, not physical entanglement observed in a particular execution. Based on this notion, we define a focused set of six metrics that characterize the amount, density, local connectivity, global scope, program span, and modular crossing of entanglement-related dependencies. We also analyze these metrics using Weyuker’s properties for evaluating software complexity measures. The paper presents the metrics as a measurement framework and research direction for empirical quantum software engineering. It discusses how future studies can validate the metrics and examine their usefulness for testing, debugging, maintenance, and program comprehension of quantum software.
Rapid growth in quantum computing has created an urgent need to adapt traditional software engineering methodologies. Among these methodological challenges, software size measurement plays a central role in project estimation, quality assurance, and software engineering management. However, there is no consolidated understanding of how it should be applied to quantum software systems. This paper presents the first systematic literature review on quantum software size measurement, synthesizing existing approaches, and identifying key research gaps. An analysis of 16 studies shows that size measures span multiple abstraction levels, with most research focusing on adapting classical metrics, such as Lines of Code (LOC) and functional size, to quantum software systems. LOC continues to be used for sizing quantum code, while COSMIC Function Points (ISO 19761) have been extended to capture data movements in quantum components, enabling their application at the requirement, code, and circuit levels. At the specification and architectural design levels, the proposed measures include counts of quantum classes and methods as well as lines of architectural specifications. At the circuit level, quantum-specific constructs, such as qubits, gates, and measurements, are also incorporated. Additionally, software structure metrics, including cyclomatic complexity and Halstead metrics, are used to measure quantum software at both the code and design levels. Research output has grown since 2024, and is driven by academic groups, with minimal industrial participation. However, most metrics remain conceptual, lack empirical validation, and are not integrated into development life cycles, highlighting the immaturity and fragmentation of current proposals and the need for standardized frameworks.
Quantum software is increasingly recognized as a critical component of quantum computing systems. As resources for quantum software design grow, the need for specialized tools and techniques to support quantum software development becomes more pressing. In this paper, we introduce the quantum program dependence graph (QPDG), a novel dependence analysis model specifically tailored for quantum software. The QPDG operates directly on the source code of quantum programs, capturing both classical and quantum-specific dependencies—namely, entanglement, no-cloning, temporal, and measurement dependencies—thus providing detailed insights into program dependencies at the code level. We further discuss key applications of QPDG, including quantum program slicing, comprehension, debugging, testing, maintenance, reuse, and complexity metrics, demonstrating its potential to enhance various aspects of quantum software engineering.
Quantum software plays a critical role in exploiting the full potential of quantum computing systems. As a result, it is drawing increasing attention recently. As research in quantum programming reaches maturity with a number of active research and practical products, software metric researchers need to focus on this new paradigm to evaluate it rigorously and quantitatively. As the first step, this paper proposes some basic metrics for quantum software, which mainly focus on measuring the size and structure of quantum software. These metrics are defined at different abstraction levels to represent various size and structure attributes in quantum software explicitly. The proposed metrics can be used to evaluate quantum software from various viewpoints.
Abstract Until now, the quality problems of quantum software have been largely ignored. This chapter analyzes the applicability of models and metrics for quantum software and, to mitigate this lack of attention to quality issues, presents a set of metrics that have been proposed and empirically validated to characterize the complexity of quantum circuits in terms of their understandability. The validation experiment design, execution, and results are reported. In addition, the main functionalities of a prototype tool that has been created for the automatic computation of the metrics are briefly presented.
Quantum computing is the basis of a new revolution. Several quantum computers are already available and, with them, quantum programming languages, quantum software development kits and platforms, quantum error correction and optimization tools are proposed and presented continuously. In connection with this, disciplines such as the Quantum Software Engineering are appearing for applying the knowledge acquired through time in their corresponding classical relatives. Besides, measurement is well known as a key factor for assessing, and improving if needed, the quality of any model in terms of, for instance, its understandability. The easier to understand a model is, the easier to maintain, reuse, etc. In this work, we present the definition of a set of metrics for assessing the understandability of quantum circuits. Some examples of the calculation of the metrics are also presented. This is just the beginning of a more thorough process in which they will be empirically validated by the performance of empirical studies, especially experiments.
As quantum computing evolves, the demand for high-quality quantum software is increasing. Classical software quality models, such as McCall's model, provide a useful foundation, but they do not explicitly capture distinctive properties of quantum software, including probabilistic measurement outcomes, backend-dependent behavior, and hybrid quantum-classical workflows. This paper proposes a base quality model for quantum software using McCall's factor-criterion-metric structure as the organizing backbone. It broadens the set of quality factors by drawing on representative classical software quality models and standards and introduces quantum-specific extensions only where needed. We define the resulting factor set and quality criteria, provide a many-to-many mapping of core factor-to-criterion relationships, discuss representative candidate metrics together with the execution, reference, and interpretation conditions needed to use them, and illustrate how the resulting model can be applied across the quantum software lifecycle. The model is intended as a common starting point for structuring, assessing, and communicating quality concerns in quantum software while supporting context-specific adaptation. Further expert feedback, case studies, and empirical studies can be used to evaluate and refine the model and its application across different quantum software contexts.