In this paper, we introduce Qsyn, a novel quantum circuit synthesis (QCS) framework designed to facilitate the research, development, testing, and experimentation of QCS algorithms and tools. Our framework is more developer-friendly than other modern QCS frameworks in three aspects: (1) Qsyn provides a comprehensive command-line interface that enables developers to design various testing scenarios with ease and conduct flexible experiments on their algorithms. This feature significantly streamlines the development process, making it more efficient and user-friendly. (2) Qsyn offers detailed access to multiple data representations at different abstraction levels of quantum circuits. This capability allows developers to optimize their algorithms extensively, gaining deeper insights and control over the structure and behavior of quantum circuits. By understanding the intricacies of circuit design, developers can achieve higher levels of optimization and performance in their algorithms. (3) Qsyn implements a rigorous development flow and environment to help developers maintain high-quality standards using modern software engineering practices, including robust quality assurance measures like regression testing, continuous integration and continuous delivery (CI/CD) pipelines, and code linting. We demonstrate Qsyn's superior performance through fair comparisons with PyZX [1], highlighting its efficiency and optimization capabilities. By providing a unified and user-friendly development environment, Qsyn empowers researchers and developers to prototype, implement, and evaluate their QCS algorithms effectively.
In this paper, we introduce Qsyn, a novel quantum circuit synthesis (QCS) framework designed to facilitate the research, development, testing, and experimentation of QCS algorithms and tools. Our framework is more developer-friendly than other modern QCS frameworks in three aspects: (1) Qsyn provides a comprehensive command-line interface that enables developers to design various testing scenarios with ease and conduct flexible experiments on their algorithms. This feature significantly streamlines the development process, making it more efficient and user-friendly. (2) Qsyn offers detailed access to multiple data representations at different abstraction levels of quantum circuits. This capability allows developers to optimize their algorithms extensively, gaining deeper insights and control over the structure and behavior of quantum circuits. By understanding the intricacies of circuit design, developers can achieve higher levels of optimization and performance in their algorithms. (3) Qsyn implements a rigorous development flow and environment to help developers maintain high-quality standards using modern software engineering practices, including robust quality assurance measures like regression testing, continuous integration and continuous delivery (CI/CD) pipelines, and code linting. We demonstrate Qsyn's superior performance through fair comparisons with PyZX [1], highlighting its efficiency and optimization capabilities. By providing a unified and user-friendly development environment, Qsyn empowers researchers and developers to prototype, implement, and evaluate their QCS algorithms effectively.
Quantum circuit synthesis is fundamental to the advancement and realization of quantum computing. To support both researchers and learners in exploring the structure and behavior of quantum circuits, this paper presents a dedicated interactive visualization platform specifically designed for quantum circuit synthesis: the Quantum Circuit Synthesis Visualizer (QSynViz). The platform offers intuitive circuit editing, matrix visualization, target-to-circuit distance computation, and dynamic tracking of the synthesis process. By making circuit structures and optimization trajectories visually accessible, QSynViz enables users to directly observe, analyze, and refine circuit designs. Bridging visual intuition with algorithmic insight, QSynViz serves as a powerful tool for circuit optimization, synthesis algorithm evaluation, and quantum computing education.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
Quantum computing leverages the principles of quantum mechanics to perform computations far beyond the capabilities of classical systems, particularly in fields such as cryptography and optimization. However, current quantum programming languages often require low-level implementation, posing significant barriers for many developers due to their steep learning curve and limited abstraction. In response, we introduce Qutes, a high-level quantum programming language designed to simplify quantum algorithm development while maintaining the flexibility required for advanced applications. By abstracting complex quantum operations and allowing intuitive expressions through high-level constructs, Qutes enables users to write efficient quantum programs without extensive knowledge of quantum mechanics or circuit design. Built upon Qiskit, Qutes translates its syntax directly into executable quantum code, facilitating seamless integration with quantum hardware. This paper provides an overview of the language's architecture, core functionalities, and its ability to unify classical and quantum operations within a single framework. Additionally, we demonstrate Qutes' application in key quantum algorithms, showcasing its potential to make quantum programming more accessible and practical for a wider range of developers and researchers.
Reproducibility and comparability remain fundamental challenges in the empirical evaluation of Quantum Software Engineering (QSE) tools. These challenges arise from the intrinsic probabilistic behavior of quantum programs and the variability introduced by quantum hardware and simulator backends. While existing quantum benchmarks typically offer only static circuit definitions, they often leave researchers responsible for data generation and execution, hindering the creation of standardized and reproducible experiments. In this paper, we introduce QSimBench, a novel, open-source benchmark suite and accompanying Python library designed to address these limitations. QSimBench provides over 20 million precomputed shot-level execution traces:20,000measurements each, across14quantum algorithms, multiple circuit sizes (4−15qubits), and six Qiskit-based backends including both ideal and noise-injected simulations. Each configuration includes not only detailed measurement histories but also the original circuit in OpenQASM, the backend's noise model, and configuration metadata, ensuring full transparency and enabling in-depth analysis. QSimBench empowers researchers to conduct scalable, reproducible, and statistically rigorous evaluations of QSE tools, such as those for monitoring, orchestration, and statistical post-processing, without the need for expensive or difficult-to-reproduce quantum executions. The benchmark is distributed with a lightweight Python API that facilitates fast data retrieval, flexible sampling strategies, and seamless integration into experimental pipelines. QSimBench is publicly available, aiming to foster collaborative and reproducible research within the quantum software community.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
The proliferation of quantum computer simulators, including leading tools such as Qiskit, BSQKit, Composer, and Quirk, highlights the growing demand for performance, usability, and accessibility in quantum computing. This project seeks to enhance an existing Java-based quantum computer simulator (QCS) developed by Naval Postgraduate School students and faculty by incorporating features that improve its accuracy, efficiency, and usability to industry standards. A comparative analysis of the performance, complexity, and usability of established simulators will provide insights into their strengths and weaknesses. Special attention will be given to understanding the intuitive design principles behind Composer and Quirk to integrate similar user-friendly features into QCS. The goal is to transform QCS into a robust and scalable simulator by introducing improvements in simulation accuracy, computational efficiency, and user interactivity, along with a valuable distinguishing feature. This initiative aims to create a tool that serves both researchers and students, offering advanced functionality while maintaining accessibility and ease of use.