The rapid evolution of quantum hardware necessitates an adaptable static analysis framework for validating quantum programs. In this work, we introduce SHARP, a rule-based static analysis framework designed for OpenQASM that decouples hardware-specific constraints from the validation engine. By employing a rule-based approach, SHARP allows quantum computing services to validate programs against evolving instruction set architectures (ISAs) without having to modify the core analysis engine. SHARP achieves this by ingesting ISA descriptions as standalone data, making the validation engine resilient to hardware updates. Furthermore, the framework employs an analysis-driven validation mechanism that enables multi-stage validation, simplifying rule construction and enhancing extensibility. We demonstrate how SHARP supports hardware-specific validation tasks and discuss its impact on quantum software development, including language decoupling, program-level constraint validation, and feature access control. Our findings suggest that SHARP provides a path toward a scalable and maintainable programming interface for quantum computing.
The rapid evolution of quantum hardware necessitates an adaptable static analysis framework for validating quantum programs. In this work, we introduce SHARP, a rule-based static analysis framework designed for OpenQASM that decouples hardware-specific constraints from the validation engine. By employing a rule-based approach, SHARP allows quantum computing services to validate programs against evolving instruction set architectures (ISAs) without having to modify the core analysis engine. SHARP achieves this by ingesting ISA descriptions as standalone data, making the validation engine resilient to hardware updates. Furthermore, the framework employs an analysis-driven validation mechanism that enables multi-stage validation, simplifying rule construction and enhancing extensibility. We demonstrate how SHARP supports hardware-specific validation tasks and discuss its impact on quantum software development, including language decoupling, program-level constraint validation, and feature access control. Our findings suggest that SHARP provides a path toward a scalable and maintainable programming interface for quantum computing.
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.
The diversity of quantum hardware platforms creates significant algorithm portability challenges, with each platform employing distinct instruction sets, gate implementations, and physical constraints that hinder software reusability. This paper presents a framework for designing portable quantum assembly languages enabling seamless cross-platform execution. Through systematic profiling of Shor’s, Grover’s, and QAOA algorithms on IBM Qiskit, Microsoft Q#, and Google Cirq — 3,216 executions of identical, reference-validated logical circuits — we identify key portability challenges and derive empirically grounded design criteria from execution-pattern analysis. With matched workloads, cross-platform circuit-depth divergence is 1.3–1.6× where no gate decomposition is required and rises to 94.8× under multi-controlled decomposition pressure, of which 3.36× is genuine same-level synthesis divergence and the remainder an abstraction-level measurement gap; optimization semantics, topology expressibility, and quantum–classical execution contracts diverge analogously. The framework balances hardware abstraction with performance optimization through declared abstraction levels, topology-aware compilation, optimization profiles with defined semantics, and a universal gate set with platform-specific decomposition. Prototype validation demonstrates 100% automated compilation of unmodified single-source programs across 30 platform configurations, with all 90 executions passing calibrated distributional acceptance gates and exact statevector fidelity of 1.000000 against the language’s reference semantics, providing feasibility evidence for vendor-independent quantum software development.
The work aims to enable the use of common software engineering techniques and tools for quantum programming languages (e.g., OpenQASM). With the increased interest in quantum computing, researchers are adopting the use of higher-level quantum programming languages versus low-level circuit diagrams. While general purpose programming languages (e.g., C++, Python) are highly supported by a variety of software engineering tools, these novel programming languages for quantum computing have almost no support. Useable tools for debugging, static analysis, error detection, and transformation are currently non-existent. This work extends an existing software infrastructure (i.e., srcML) for the analysis, exploration, and manipulation of source code to OpenQASM. The srcML infrastructure, via parsing, generates abstract syntax information of programs to support high-level querying and analysis of the source code. With this, quantum developers can extract information and identify possible errors or inefficiencies in their programs. The paper presents the basic syntactic markup for OpenQASM. Also, a number of relevant quantum-based problems (e.g., iteration patterns, control recursion) are described and examples of how they are addressed using srcML are given.
Quantum computing is advancing rapidly, with developers increasingly adopting quantum programming languages. However, despite progress in quantum research, there is a notable lack of development tools and technologies that assist developers in designing and analyzing quantum programs. This work aims to address this gap using a software engineering approach. Specifically, we leverage the srcML infrastructure to automate the static analysis and refactoring of OpenQASM quantum programs by implementing a tool, QStatic. We then demonstrate QStatic’s potential by identifying and refactoring common code patterns. Our approach enables the exploration and manipulation of quantum source code, effectively addressing numerous challenges in quantum programming, such as optimizing quantum gates to reduce runtime costs.
Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
While significant progress has been made on the hardware side of quantum computing, support for high-level quantum programming abstractions remains underdeveloped compared to classical programming languages. In this article, we introduce Qrisp, a framework designed to bridge several gaps between high-level programming paradigms in state-of-the-art software engineering and the physical reality of today's quantum hardware. The framework aims to provide a systematic approach to quantum algorithm development such that they can be effortlessly implemented, maintained and improved. We propose a number of programming abstractions that are inspired by classical paradigms, yet consistently focus on the particular needs of a quantum developer. Unlike many other high-level language approaches, Qrisp's standout feature is its ability to compile programs to the circuit level, making them executable on most existing physical backends. The introduced abstractions enable the Qrisp compiler to leverage algorithm structure for increased compilation efficiency. Finally, we present a set of code examples, including an implementation of Shor's factoring algorithm. For the latter, the resulting circuit shows significantly reduced quantum resource requirements, strongly supporting the claim that systematic quantum algorithm development can give quantitative benefits.