ABSTRACT Introduction The rapid growth of quantum computing frameworks such as Qiskit, Cirq, and Amazon Braket has accelerated quantum software development but has also introduced fragmentation in tools, syntax, and workflows. Methods This paper presents a lightweight and extensible interface for building quantum circuits from tokenized text input, enabling rapid prototyping and cross‐framework compatibility. The proposed system defines a simple domain‐specific language composed of quantum instruction tokens (e.g., H0, CNOT0‐1) that are parsed into a unified intermediate representation. This intermediate form is then translated into executable quantum circuits across multiple backends using a modular translator architecture. In addition, the system supports user‐defined gate decompositions through a configurable mapping mechanism. Results The proposed system is implemented in Python and evaluated across three major quantum software development kits. Experimental results demonstrate consistent and correct circuit generation across all supported backends. Conclusions The approach promotes abstraction, reusability, and readability in the quantum software development lifecycle, providing a practical solution to mitigate fragmentation across quantum programming frameworks.
ABSTRACT Introduction The rapid growth of quantum computing frameworks such as Qiskit, Cirq, and Amazon Braket has accelerated quantum software development but has also introduced fragmentation in tools, syntax, and workflows. Methods This paper presents a lightweight and extensible interface for building quantum circuits from tokenized text input, enabling rapid prototyping and cross‐framework compatibility. The proposed system defines a simple domain‐specific language composed of quantum instruction tokens (e.g., H0, CNOT0‐1) that are parsed into a unified intermediate representation. This intermediate form is then translated into executable quantum circuits across multiple backends using a modular translator architecture. In addition, the system supports user‐defined gate decompositions through a configurable mapping mechanism. Results The proposed system is implemented in Python and evaluated across three major quantum software development kits. Experimental results demonstrate consistent and correct circuit generation across all supported backends. Conclusions The approach promotes abstraction, reusability, and readability in the quantum software development lifecycle, providing a practical solution to mitigate fragmentation across quantum programming frameworks.
Designing and prototyping quantum circuits remains challenging for beginners and intermediate users due to steep learning curves in text-based frameworks and limited features in existing visual tools. This paper introduces QuantumFlow, a web-based platform that addresses these challenges through an accessible drag-and-drop interface for constructing and simulating quantum circuits. The platform provides realtime validation, state visualization, and interactive Bloch-sphere rendering to enable both conceptual exploration and rapid prototyping. Key features include modular subcircuit design, mid-circuit measurements, export to Qiskit, Cirq, and JSON, and hardware-aware transpilation for processors such as IBM's Falcon and Google's Sycamore. Implemented with React and Redux, the platform runs entirely client-side without installation. A structured evaluation with14participants found that QuantumFlow users completed circuit construction tasks45%faster and scored 38 % higher on comprehension assessments than peers relying on textbook workflows. Exported code aligns with framework standards and incorporates optimizations including gate fusion, commutation, and device-specific mapping. Current limitations involve scalability constraints and the absence of direct backend connectivity. Overall, QuantumFlow enhances accessibility to quantum computing education, supports interactive learning, and accelerates early-stage development, effectively bridging instructional platforms with advanced frameworks.
The rapid growth of quantum computing calls for more accessible, interoperable, and collaborative development environments. While current quantum providers allow remote execution of quantum circuits, developers still face significant challenges due to fragmented programming models, lack of standardization, and limited support for collaboration. In response to these issues, this work introduces a web-based tool that integrates three key capabilities: visual circuit design, realtime collaborative editing, and deployment of quantum circuits as services. This feature enables multiple developers to codesign quantum circuits synchronously, in the spirit of cloudbased collaborative tools. This functionality facilitates teamwork, education, and reproducibility in quantum software engineering by allowing users to edit, share, and review quantum circuits simultaneously. In addition to collaborative editing, the tool also allows developers to automatically translate circuits into code compatible with both Qiskit and Amazon Braket, execute them on real quantum back-ends, and visualize execution results.
Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.
We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit execution, and results analysis into independent, interoperable components. Supporting over 20 benchmark variants ranging from simple algorithmic tests like Bernstein-Vazirani to complex Hamiltonian simulation with observable calculations, the system integrates with multiple circuit generation APIs (Qiskit, CUDA-Q, Cirq) and enables diverse workflows. We validate the architecture through successful integration with Sandia's pyGSTi for advanced circuit analysis and CUDA-Q for multi-GPU HPC simulations. Extensibility of the system is demonstrated by implementing dynamic circuit variants of existing benchmarks and a new quantum reinforcement learning benchmark, which become readily available across multiple execution and analysis modes. Our primary contribution is identifying and formalizing modular interfaces that enable interoperability between incompatible benchmarking frameworks, demonstrating that standardized interfaces reduce ecosystem fragmentation while preserving optimization flexibility. This architecture has been developed as a key enhancement to the continually evolving QED-C Application-Oriented Performance Benchmarks for Quantum Computing suite.
We present the first universal quantum compiler based on Generative Pre-trained Transformer (GPT) architecture that provides cross-framework optimization and translation capabilities for quantum circuits. Our Quantum Compiler GPT (QC-GPT) unifies four major quantum computing frameworks—Qiskit Terra, Cirq, tket and PyQuil— into a single Al-driven compilation system. The model demonstrates significant improvements in circuit optimization, achieving 23-47% reduction in gate count, 18-35% reduction in circuit depth, and 12-28% improvement in estimated fidelity compared to existing compilers. QC-GPT also enables seamless translation between quantum frameworks with 96.3% accuracy while providing human-readable explanations for optimization decisions. Our approach leverages a quantum-aware transformer architecture with 512M parameters, trained on 104,000 circuit optimization examples across all supported frameworks. This work represents the first successful application of large language models to universal quantum compilation, opening new possibilities for Al-assisted quantum software development.
Programming quantum computers is a complex endeavor that requires highly specialized knowledge. Given the growing momentum of quantum computing, high-level synthesis of quantum circuits from well-known classical languages could significantly benefit the ecosystem, allowing specialists to focus on the parts of an application where quantum computing offers the greatest advantage, while letting the compiler handle the boilerplate code. To bridge this gap, we present a high-level synthesis framework that translates C programs into quantum circuits through a sequence of modular and structured Multi-Level Intermediate Representation (MLIR) dialects. These dialects expose classical control and data flow in a form compatible with quantum constraints, enabling reversible and unitary circuit generation. Our framework maps conditionals, loops, and arithmetic operations, supports configurable bitwidths, and allows retargetability to different quantum backends. Furthermore, we demonstrate a correlation between classical program metrics—such as instruction count and cyclomatic complexity—and the performance characteristics of the resulting quantum circuits. To validate our methodology, we quantify compilation resource usage, showing a linear scaling of memory footprint and a quadratic scaling of CPU time as a function of quantum circuit complexity. Our tool generates circuits in OpenQASM format, facilitating integration with current quantum programming frameworks.