Building the Quantum Software Stack

by

Author: Denis Avetisyan

A new track at JISBD 2025 spotlights the burgeoning field of quantum software engineering and the challenges of bringing quantum computing to life.

This review summarizes the inaugural QuantumX workshop, outlining key research areas including quality metrics, hybrid quantum-classical architectures, and software abstraction for a growing Spanish research community.

While the potential of quantum computation is increasingly recognized, translating theoretical advances into reliable and scalable quantum software remains a significant challenge. This paper details the inaugural QuantumX: an experience for the consolidation of Quantum Computing and Quantum Software Engineering as an emerging discipline track, held at JISBD 2025, which brought together Spanish researchers focused on bridging this gap. Key contributions explored areas from quantum service engineering and hybrid architectures to quality metrics and abstraction layers necessary for effective quantum software development. Given the momentum building within the Spanish quantum software ecosystem – exemplified by initiatives like RIPAISC and QSpain – what are the most pressing engineering concerns and open challenges that must be addressed to realize the full potential of this emerging field?

The Emerging Constraints of Quantum Software

Despite the immense potential of quantum computation to revolutionize fields like medicine, materials science, and artificial intelligence, realizing these breakthroughs is currently constrained by limitations in the software ecosystem. Existing programming languages and development tools are ill-equipped to handle the unique demands of quantum hardware, which operates on principles drastically different from classical computers. This necessitates painstakingly complex code to manage quantum states and algorithms, increasing the likelihood of errors and hindering the creation of scalable quantum applications. The nascent stage of quantum software development means researchers and engineers spend significant effort overcoming tooling challenges rather than focusing on algorithm design and problem-solving, effectively slowing the pace of innovation and delaying the widespread adoption of this transformative technology.

The transition from classical to quantum computation presents significant hurdles for software development, as existing programming paradigms are ill-equipped to handle the inherent complexities of quantum hardware. Unlike bits representing 0 or 1, quantum bits, or qubits, leverage superposition and entanglement – phenomena that demand fundamentally different approaches to code construction. Consequently, even relatively simple quantum algorithms often require extensive and intricate code, prone to errors arising from managing qubit states and mitigating decoherence. This complexity isn’t merely a matter of increased lines of code; the very logic of quantum programs diverges from classical counterparts, necessitating new debugging techniques and verification methods. The resulting software is often difficult to write, understand, and maintain, hindering progress and limiting the ability to scale quantum applications beyond proof-of-concept demonstrations.

The path to realizing quantum computing’s transformative potential is significantly hindered by a critical need for software abstraction. Currently, programmers must grapple with the intricacies of quantum mechanics and hardware-specific limitations, resulting in code that is notoriously complex and prone to errors. Higher levels of abstraction – analogous to those found in classical software development – would shield programmers from these low-level details, allowing them to focus on algorithm design and problem-solving. This simplification isn’t merely about ease of use; it’s essential for scalability. As quantum processors increase in qubit count and complexity, manually managing every quantum operation will become impossible. Effective abstraction layers will enable the development of robust, maintainable quantum software, fostering wider adoption and ultimately unlocking the technology’s full capabilities for tackling previously intractable problems in fields like medicine, materials science, and artificial intelligence.

Architecting Reliability: The Rise of Quantum Service Engineering

Quantum Service Engineering represents a nascent field focused on applying established software engineering methodologies – including requirements analysis, design, implementation, testing, and maintenance – to the unique challenges of developing quantum applications. This discipline addresses the need for structured development practices given the inherent complexities of quantum hardware and algorithms. Key considerations include managing quantum resources, handling error mitigation strategies, and ensuring the reliable integration of quantum processing units (QPUs) with classical computing infrastructure. The goal is to move beyond prototype quantum programs toward production-ready, scalable, and maintainable quantum services that can be consistently delivered to end-users, mirroring the maturity found in conventional software development.

The HARMONIA subproject demonstrates the integration of quantum and classical computing resources through the creation of a unified computational continuum. This architecture allows for the seamless execution of hybrid algorithms, where tasks are dynamically allocated to either quantum processing units (QPUs) or classical central processing units (CPUs) based on their suitability. Specifically, HARMONIA utilizes a software stack designed to abstract the complexities of underlying hardware, presenting a single programming environment for developers. This facilitates the orchestration of quantum kernels-small, quantum-executable functions-within larger classical applications, enabling resource optimization and efficient data transfer between quantum and classical domains. The project’s focus is on establishing standardized interfaces and protocols to manage the interaction between these heterogeneous computing resources, forming a cohesive system for scalable quantum service delivery.

Prioritizing quality and maintainability in quantum service development necessitates the implementation of established software engineering practices, including comprehensive testing, version control, and modular design. This approach facilitates the creation of robust quantum services capable of consistent performance and predictable behavior. Scalability is achieved through abstraction layers that decouple quantum hardware from application logic, enabling adaptation to evolving quantum technologies and increased computational demands. The resulting services are intended for integration into a broad range of applications, including optimization, machine learning, and materials discovery, and are designed to minimize technical debt and ensure long-term operational viability.

Validating Quantum Systems: A Multi-Faceted Approach to Quality Assurance

Research groups including ITIS, Alarcos, and Quercus are actively developing methodologies to address the unique quality assurance challenges presented by quantum computing. This work centers on the creation of high-level abstractions to simplify complex quantum operations, the definition of comprehensive quality models tailored for quantum software, and the implementation of robust software governance frameworks. These efforts aim to move beyond low-level gate manipulation towards more manageable and verifiable quantum programs, enabling systematic testing and improved reliability as quantum systems scale in complexity. The focus is on establishing best practices for the development lifecycle, mirroring established software engineering principles but adapted to the specific characteristics of quantum computation.

The Quantum Service Engineering Process integrates conventional software engineering practices by adopting established standards like OpenAPI for interface definition and utilizing static analysis tools such as SonarQube for continuous inspection of quantum code. This approach enables automated code review, identification of potential vulnerabilities, and enforcement of coding standards, mirroring quality assurance workflows in classical software development. Leveraging these existing tools and standards reduces the need for entirely new quality control methodologies specific to quantum computing and facilitates a more streamlined, repeatable, and scalable development lifecycle for quantum services.

Optimization and testing of quantum circuit planning are facilitated through the use of techniques including Quantum Mutants and the QCRAFT Scheduler. Implementation of these methods has demonstrated significant cost reductions in quantum computing scenarios; specifically, testing on IBM Quantum platforms yielded approximately a 94% cost decrease, while utilization of the QCRAFT Scheduler alone resulted in an approximate 84% reduction in costs compared to executing circuits individually. These cost savings are achieved through improved circuit optimization and scheduling, minimizing the resources required for computation.

Forging a Quantum Ecosystem: Collaboration and the Democratization of Access

The QuantumX Track functions as a central, nationwide platform designed to bridge the historically separate fields of quantum computing and software engineering. This initiative actively cultivates a collaborative environment where researchers, developers, and industry professionals can converge, share expertise, and collectively address the significant challenges inherent in building practical quantum applications. By fostering open communication and knowledge exchange, the Track aims to accelerate innovation, standardize development practices, and ultimately democratize access to quantum technologies. This convergence isn’t merely about combining disciplines; it represents a fundamental shift toward a more integrated approach, ensuring that advancements in quantum hardware are effectively translated into tangible software solutions and impactful real-world applications.

Dedicated research collectives – including PAPC, COGRADE, eVIDA, and ROBÓTICA – are actively pushing the frontiers of quantum computing by concentrating on practical applications within machine learning and data processing. These groups aren’t simply exploring theoretical possibilities; they are building tangible advancements in areas like quantum algorithms for enhanced data analysis and the development of novel machine learning models leveraging quantum principles. Their combined efforts focus on overcoming current limitations in computational power and efficiency, specifically targeting challenges where classical computers struggle, such as complex pattern recognition and large-scale data optimization. This concentrated approach promises to unlock new capabilities in fields ranging from financial modeling and drug discovery to materials science and artificial intelligence, ultimately broadening the scope of quantum computing beyond specialized research into broadly applicable solutions.

Density Matrix Emulation is proving instrumental in broadening the scope of quantum machine learning. This technique allows researchers to simulate quantum systems on classical hardware, effectively bypassing the limitations imposed by the scarcity of readily available quantum computers. By accurately representing quantum states as density matrices, even complex quantum algorithms can be prototyped and refined without requiring immediate access to quantum hardware. This not only accelerates the development cycle for Quantum Machine Learning models but also allows for more extensive testing and optimization, ultimately expanding the utility and accessibility of these emerging services for a wider range of applications – from pattern recognition and data analysis to advanced materials discovery and financial modeling.

The inaugural QuantumX track at JISBD 2025, as detailed in this paper, signals a maturing field demanding holistic consideration. It’s not simply about optimizing quantum circuits or developing novel algorithms; it’s about understanding the interplay between quantum and classical systems, and establishing robust quality metrics for evaluating their combined performance. This echoes Robert Tarjan’s insight: “Structure dictates behavior.” The emerging discipline of Quantum Software Engineering, with its focus on software abstraction and hybrid architectures, necessitates a systemic approach. One cannot effectively address challenges in, say, quantum machine learning without considering the implications for the entire software stack and the underlying quantum-classical infrastructure. The entire system’s efficacy depends on the coherence of its design.

Where Do We Go From Here?

The proceedings summarized here reveal a nascent field grappling with its own foundations. Quantum Software Engineering, as evidenced by the QuantumX track, rightly focuses on the practicalities of a technology perpetually ‘just over the horizon’. Yet, a subtle tension exists. The drive for abstraction – for higher-level programming models – is necessary, but risks obscuring the very hardware constraints that define the landscape. Systems break along invisible boundaries – if one cannot see them, pain is coming. The current emphasis on quality metrics is commendable, but metrics alone are insufficient; they measure what is broken, not why.

The exploration of hybrid quantum-classical architectures is, predictably, central. This approach acknowledges the limitations of current quantum devices, but also introduces a new layer of complexity. The interface between classical and quantum realms will become a critical point of failure, and the tools for debugging such interactions are conspicuously absent. A holistic view is required; one cannot optimize the quantum portion of the system in isolation, ignoring the classical overhead.

Ultimately, the field needs to move beyond simply building quantum software and begin to understand its inherent limitations. The pursuit of elegant design demands simplicity, and that simplicity must be rooted in a clear understanding of the underlying physics. The future of Quantum Software Engineering will not be defined by clever abstractions, but by a willingness to confront the unavoidable constraints of the quantum world.

Original article: https://arxiv.org/pdf/2603.10621.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-03-12 06:13