Accurate sizing of software systems is essential for effective project management and resource allocation. Hybrid classical-quantum software systems consist of a quantum component, which executes quantum algorithms and incorporates error correction mechanisms (overhead)-a critical aspect in the Noisy Intermediate-Scale Quantum (NISQ) era-and a classical component, which controls quantum operations and manages error propagation. This paper explores functional sizing for hybrid classical-quantum software. It highlights the challenges arising from the interaction between the classical and quantum components, particularly with respect to the abstraction layers and granularity levels. A key principle emphasized is that functional sizing should not extend across multiple abstraction layers within a single measurement scope. While quantum software typically operates at a low-level implementation-specific layer, classical software generally functions at a higher level of abstraction. This divergence emphasizes the need to clarify how functional size measurement should be applied using set theory to account for requirements specified at different abstraction layers in hybrid computing environments.
Accurate sizing of software systems is essential for effective project management and resource allocation. Hybrid classical-quantum software systems consist of a quantum component, which executes quantum algorithms and incorporates error correction mechanisms (overhead)-a critical aspect in the Noisy Intermediate-Scale Quantum (NISQ) era-and a classical component, which controls quantum operations and manages error propagation. This paper explores functional sizing for hybrid classical-quantum software. It highlights the challenges arising from the interaction between the classical and quantum components, particularly with respect to the abstraction layers and granularity levels. A key principle emphasized is that functional sizing should not extend across multiple abstraction layers within a single measurement scope. While quantum software typically operates at a low-level implementation-specific layer, classical software generally functions at a higher level of abstraction. This divergence emphasizes the need to clarify how functional size measurement should be applied using set theory to account for requirements specified at different abstraction layers in hybrid computing environments.
The rapid evolution of quantum computing has led to growing interest in the development of systematic approaches to assess and manage quantum software. Among these, functional size measurement (FSM) offers a promising pathway for establishing metrics that can support project estimation, benchmarking, and quality assessment. This paper provides an overview of emerging efforts in quantum software functional size measurement with a focus on key concepts, challenges, and motivations. First, we examine the foundational principles of quantum computing in relation to the existing FSM methods, identifying both compatibilities and unique constraints. Building on this, we discuss the challenges that arise when applying FSM to quantum software, including the hardware-coupled nature of functionality, evolving semantics of data and measurement abstractions, gaps in tooling and standardization, and evolving role of FSM across the Noisy Intermediate-Scale Quantum (NISQ) and future faulttolerant eras. We then outline community-driven initiatives, such as the COSMIC Quantum Software Taskforce and the Fall 2024 Workshop, which highlight the increasing demand for structured measurement practices. By articulating these issues, this paper aims to present initial research efforts for quantum software functional size measurement and stimulate further exploration of measurement approaches that are both theoretically grounded and practically applicable in quantum software engineering.
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.
The Noisy Intermediate-Scale Quantum (NISQ) era, defined by qubit counts in the hundreds and gate error rates that preclude long coherence times, imposes severe constraints on purely quantum computation. The central problem is that achieving practical quantum advantage for real-world tasks must overcome these hardware limitations, yet fault-tolerant quantum computers with full error correction remain years away. To address this, we present a systematic survey of hybrid classical-quantum computing models, in which classical high-performance resources orchestrate the workflow, perform iterative optimization, and apply error mitigation to noisy quantum processing units. Our review encompasses variational quantum algorithms, such as the variational quantum eigensolver and quantum approximate optimization algorithm; classical optimizers tailored for noisy quantum landscapes; error mitigation techniques including zero-noise extrapolation and probabilistic error cancellation; and circuit knitting that partitions large circuits across small QPUs. We find that these hybrid architectures have already demonstrated domain-specific utility in quantum chemistry for molecular ground-state estimation, in logistics and finance for combinatorial optimization, and in machine learning for generative and recommendation models. Notably, they often surpass purely classical or purely quantum approaches on carefully chosen benchmarks. The evidence indicates that hybrid classical-quantum systems are not a temporary stopgap but a durable paradigm. Their role will persist into the early fault-tolerant era, where small, logical qubits will similarly require classical coordination. Consequently, the field urgently demands the establishment of standardized hybrid performance benchmarks, the maturation of robust quantum-classical middleware capable of efficient transpiration and execution management, and holistic co-design frameworks that seamlessly integrate hardware constraints, software stack layers, and application-specific requirements from the outset.
Quantum computers represent a radical technological advancement in the way information is processed by using the principles of quantum mechanics to solve very complex problems that exceed the capabilities of classical systems. However, in the current NISQ era (Noisy Intermediate-Scale Quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and reduced coherence times. Efficient management of quantum resources, both physical (qubits, error rates, connectivity) and logical (quantum gates, algorithms, error correction), becomes particularly relevant in the design and deployment of quantum algorithms. In this work, we analyze the role of resources in the various uses of NISQ devices today, identifying their relevance and implications for software engineering focused on the use of quantum computers. We propose a vision for runtime-aware quantum software development, identifying key challenges to its realization, such as limited introspection capabilities and temporal constraints in current platforms. As a proof of concept, we introduce Qonscious, a prototype framework that enables conditional execution of quantum programs based on dynamic resource evaluation. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move towards the development of scalable, reliable, and resource-aware quantum software.
Quantum computing promises to offer significant computational advantages over classical computing, leveraging principles such as superposition and entanglement. This necessitates effective metrics and measurement techniques for evaluating quantum systems, aiding in their development and performance optimization. However, due to fundamental differences in computing paradigms, and current immaturity of quantum software abstractions, classical software and hardware metrics may not directly apply to quantum computing, where the distinction between software and hardware can still be somewhat indiscernible compared to classical computing. This paper provides a comprehensive review of existing quantum software and hardware metrics in the scientific literature, highlighting key challenges in the field. Additionally, it investigates the application of Functional Size Measurement (FSM) based on COSMIC ISO 19761 to measure quantum software. Three FSM approaches are analyzed by applying them to Shor’s and Grover’s algorithms, with measurement results compared to assess their effectiveness. A comparative analysis highlights the strengths and limitations of each approach, emphasizing the need for further refinement. The insights from this study contribute to the advancement of quantum metrics, especially software metrics and measurement, paving the way for the development of a unified and standardized approach to quantum software measurement and assessment.
Quantum computers represent a radical technological breakthrough in information processing by leveraging the principles of quantum mechanics to solve highly complex problems beyond the reach of classical systems. However, in the current NISQ era (noisy intermediate-scale quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and short coherence times. Efficient management of quantum resources, both physical and logical, is especially relevant in the design and deployment of quantum algorithms. In this paper, we analyze the role of resources in current uses of NISQ devices, identifying their relevance and implications for quantum software engineering. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move toward scalable and reliable quantum software development