Quantum computing research generates large volumes of experimental data that require systematic storage, retrieval, and analysis. However, lightweight and accessible infrastructure for managing such data remains underexplored outside of large research institutions. This paper presents the design and implementation of a research data pipeline for quantum computing experiments. The system integrates a quantum circuit simulator, a REST API for data ingestion and retrieval, a structured metadata database, a statistical analysis module, and an automated PDF report generation system. The platform is containerized using Docker and validated through automated tests. The goal is to demonstrate how modern backend engineering tools can be applied to build reproducible and scalable research data infrastructure accessible to individual researchers and small teams.
Quantum computing research generates large volumes of experimental data that require systematic storage, retrieval, and analysis. However, lightweight and accessible infrastructure for managing such data remains underexplored outside of large research institutions. This paper presents the design and implementation of a research data pipeline for quantum computing experiments. The system integrates a quantum circuit simulator, a REST API for data ingestion and retrieval, a structured metadata database, a statistical analysis module, and an automated PDF report generation system. The platform is containerized using Docker and validated through automated tests. The goal is to demonstrate how modern backend engineering tools can be applied to build reproducible and scalable research data infrastructure accessible to individual researchers and small teams.
As quantum computing evolves from theoretical promise to practical deployment, the demand for robust, portable, and scalable tools for quantum software experimentation is growing. This paper introduces Quantum Executor, a backend-agnostic execution engine designed to orchestrate quantum experiments across heterogeneous platforms. Quantum Executor provides a declarative and modular interface that decouples experiment design from backend execution, enabling seamless interoperability and code reuse across diverse quantum and classical resources. Key features include support for asynchronous and distributed execution, customizable execution strategies and a unified API for managing quantum experiments. We illustrate its applicability through two life-like usage scenarios such as automated benchmarking and hybrid validation, discussing its capacity to streamline quantum development. We conclude by discussing current limitations and outlining a roadmap for future enhancements.
Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting.
We introduce the Quantum Experiment Framework (QEF): A lightweight framework designed to support the systematic, hypothesis-driven study of quantum algorithms. Unlike many existing approaches, QEF emphasises iterative, exploratory analysis of evolving experimental strategies rather than exhaustive empirical evaluation of fixed algorithms using predefined quality metrics. The framework's design is informed by a comprehensive review of the literature, identifying principal parameters and measurement practices currently reported in the field.
QEF captures all key aspects of quantum software and algorithm experiments through a concise specification that expands into a Cartesian product of variants for controlled large-scale parameter sweeps. This design enables rigorous and systematic evaluation, as well as precise reproducibility. Large sweeps are automatically partitioned into asynchronous jobs across simulators or cloud hardware, and ascertain full hyper-parameter traceability. QEF supports parameter reuse to improve overall experiment runtimes, and collects all metrics and metadata into a form that can be conveniently explored with standard statistical and visualisation software.
By combining reproducibility and scalability while avoiding the complexities of full workflow engines, QEF seeks to lower the practical barriers to empirical research on quantum algorithms, whether these are designed for current NISQ devices or future error-corrected quantum systems.
The advent of quantum computing has ushered in a new era of technological innovation, promising unprecedented computational power and efficiency. As quantum technology matures, the need for robust, scalable, and efficient software development practices becomes increasingly critical. This paper proposes a novel DevOps framework tailored specifically for quantum software development, addressing unique challenges posed by quantum computing. The proposed architecture incorporates continuous integration and continuous deployment (CI/CD) pipelines, optimized for quantum algorithms, facilitating seamless integration and deployment of quantum applications. Key components include a Quantum Circuit Service for circuit design and optimization, a Quantum CI Service for continuous integration, and a comprehensive testing suite encompassing unit, integration, functional, and probabilistic testing. The framework emphasizes the use of large language models (LLMs) for automated microservice code generation, Kubernetes (K8s) cluster deployment for scalable infrastructure management, and a central quantum artifacts registry for efficient version control and resource management. A significant portion of this paper is dedicated to addressing the unique aspects of quantum computing, including the probabilistic nature of quantum algorithms, the need for specialized testing strategies, and the integration of classical and quantum software components. The proposed framework not only enhances the efficiency and reliability of quantum software development but also provides a structured approach to managing the complexities associated with quantum computing.
Quantum Computing allows for substantial speed-ups e.g. for integer factorization or database search, compared to conventional computation. However, since real quantum computers are an emerging technology, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This paper proposes a containerized environment to simulate quantum circuits, making this environment quick and easy to share within research teams. Also test the environment implementing an arithmetic circuit (Vedral’s adder) and compares the performance of two of the current more used environments: IBM QISKit and Google Cirq.
As quantum computing continues to progress, there is an increasing need for standardized quantum network technology [1], [2] to connect quantum computers together. Experimental testbeds are necessary in order to develop quantum network software and hardware. Building a scalable testbed for data center-level quantum networks requires overcoming numerous engineering and technical challenges in quantum communications, quantum processing and quantum storage. In this work, we describe a software framework for use with an all-photonic quantum networking testbed. This framework is designed for rapid development and capable of automated execution of quantum experiments. We introduce PnPQ, an open-source, unified driver library for hardware commonly used in quantum optical experiments. On top of this low-level hardware control library, we present software modules for controlling and coordinating quantum network nodes using remote procedure calls (RPC). Finally, we describe high-level software for performing and analyzing quantum photonic experiments using these software modules. As a whole, these software components comprise the first version of a framework that will enable us to implement and test quantum network architectures. This framework is intended to help us move forward from building simple experiments to complex Quantum systems.
Quantum computing has emerged as a promising tool for transforming the landscape of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.