quantum fidelity estimation

This study addresses the lack of effective metrics for analyzability in quantum-classical hybrid software, a key barrier to its maintainability and industrial adoption. Building upon the ISO/IEC 25010 standard, the authors propose a novel analyzability model for quantum software and empirically validate its ability to differentiate analyzability across diverse quantum algorithms through four controlled experiments involving multiple scenarios and participant groups. Complementing these experiments with user surveys on perceived complexity, the study demonstrates a strong alignment between the model’s outputs and human subjective judgments. The findings indicate that the proposed model not only provides an effective means to quantify analyzability in quantum software but also establishes a meaningful correspondence between objective measurement and human cognition, thereby offering a reliable tool for quantum software quality assessment.

Quantum program verification on early fault-tolerant hardware faces critical challenges due to scarce measurement resources and tight measurement budgets. Method: We propose the first unified, program-level measurement budgeting framework that systematically links theoretical error bounds—based on trace distance, fidelity, error probability, and the quantum Chernoff bound—with practical testing strategies: inversion testing, swap testing, and chi-square testing. The framework supports scalable analysis, from single-gate verification to full-program validation. Contribution/Results: We quantify substantial measurement overhead differences among strategies: inversion testing is optimal; swap testing incurs roughly 2× overhead; chi-square testing is simple but costly. Noise and fine-grained circuit decomposition further escalate costs. To address this, we introduce coarse-grained partitioning and weighted budget allocation, achieving superior trade-offs between verification accuracy and hardware resource consumption. Our framework establishes a computationally tractable, deployable paradigm for quantifying and allocating measurement resources in quantum software verification.

This work systematically addresses the verifiability and quantifiability of quantum “magic”—a fundamental resource underpinning quantum computational advantage. We propose a scalable stabilizer verification protocol based on quantum convolution and swap testing, reducing the verification complexity for quantum states and gates to polynomial time. We introduce the novel concept of “magic entropy,” the first experimentally measurable magic monotone that is both monotonic under Clifford operations and convex—overcoming the long-standing limitation that magic cannot be directly observed. Through theoretical analysis, design of universal circuits (for both qubits and qudits), and numerical simulations, we demonstrate that magic entropy exhibits high sensitivity to deviations from the Clifford group. Our framework establishes a new paradigm for benchmarking and hardware validation of magic resources in near-term quantum devices.

Quantum software complexity poses significant challenges to the detection of state discrepancies and reliability in unit testing. To address this, we propose a quantum-centric unit testing framework specifically designed for quantum circuits, featuring a novel inverse test technique integrated with statevector testing and SWAP testing—yielding a hybrid verification methodology compatible with both classical simulation and quantum hardware execution. Empirical evaluation across over 1.79 million mutated circuits demonstrates that our approach substantially reduces both false positive and false negative rates compared to conventional statistical testing. Moreover, statevector and inverse tests achieve high-confidence verification with significantly fewer measurements and markedly improved fault detection capability. This work establishes a new unit testing paradigm for quantum software that offers superior accuracy and efficiency in verification.

To address challenges in quantum program testing—including the difficulty of quantum state quantification, absence of symbolic modeling, inefficient path exploration, and insufficient branch coverage—this paper proposes the first concolic (combined concrete and symbolic) testing framework tailored for quantum programs. Methodologically, it introduces a novel constraint modeling mechanism for quantum control statements, designs symbolic representations for quantum variables, and develops a dedicated quantum constraint-solving strategy; it further integrates Qiskit to enable quantum-state quantification and constraint-driven path guidance. Experimental evaluation on programs with up to five qubits achieves a branch coverage of 74.27%, substantially outperforming baseline approaches. Moreover, the framework generates higher-quality quantum input samples, effectively exposing logical errors and quantum gate sequence defects. This work establishes a scalable symbolic execution paradigm and provides a practical toolset for quantum software testing.

This work addresses a critical gap in current quantum computing education, which predominantly emphasizes algorithms and framework usage while neglecting core software engineering challenges such as testing, abstraction, toolchain integration, and lifecycle management. To bridge this gap, the authors designed and implemented an interdisciplinary course for undergraduate and graduate students that approaches quantum computing from a software engineering perspective, integrating foundational quantum information concepts, algorithm implementation, and hands-on engineering practice. The curriculum emphasizes executable artifacts, empirical reasoning, and engineering trade-offs in noisy environments. Structured modularly and supported by a scalable assessment model, the course effectively accommodates students with diverse academic backgrounds. Empirical results demonstrate that even learners without prior quantum computing experience can grasp key concepts and engage meaningfully in engineering tasks, thereby validating the feasibility and transferability of this pedagogical approach.

This study addresses a critical gap in quantum software research: the absence of a systematic auditing mechanism for empirically grounded comparative claims, which has led to a pervasive “instantiation gap” characterized by insufficient evidentiary support. To bridge this gap, the authors propose CLAIMSTAB-QC, the first source-bound auditing framework tailored to empirical comparisons in quantum software. By integrating claim modeling, audit scope delimitation, evidence boundary identification, and directional classification, the framework enables precise validation of comparative assertions against original source materials. An evaluation across 455 claims from 119 papers reveals that only eight claims possessed sufficient matched evidence for direct auditing; among these, two were confirmed, four lacked adequate support, and two were contradicted—highlighting substantial deficiencies in the empirical rigor of current quantum software studies.

This work addresses the lack of reproducible and fairly comparable benchmarks in quantum software testing, which has largely relied on small, hard-coded circuits that poorly reflect real-world development practices. To bridge this gap, the authors introduce Qolumbina—the first scalable benchmark suite for quantum software testing—systematically curated from 40 representative programs sourced from open-source repositories and rigorously refactored with standardized interfaces, test cases, and formal specifications. The study further proposes a novel taxonomy of testing characteristics specific to quantum programs and employs program complexity modeling to enable scalability analysis and systematic evaluation. Covering a diverse range of testing attributes, Qolumbina has already facilitated empirical studies on execution overhead and fault detection capability, revealing the critical influence of backend dependencies on the interpretation of testing outcomes.

This work addresses the long-standing oracle problem in quantum software stack (QSS) testing—the lack of reliable reference outputs for expected behavior—by introducing QEMI, the first testing methodology to adapt Equivalence Modulo Inputs (EMI), a technique from classical compiler testing, to the quantum domain. QEMI generates random quantum programs containing dead code and constructs semantically equivalent variants, then detects QSS bugs by comparing their execution outcomes across different platforms. Unlike conventional approaches that rely on syntactic or structural transformations, QEMI enables semantic-preserving program mutation and analysis. Empirical evaluation on Qiskit, Q#, and Cirq uncovered 11 crash-inducing bugs and one behavioral inconsistency, substantially enhancing the effectiveness of quantum software stack testing.

This work addresses the lack of systematic experimental tracking in current quantum software development, which hinders effective monitoring of hardware noise, software evolution, and error sources. It introduces, for the first time, a holistic experimental tracking methodology tailored to quantum characteristics, proposing an end-to-end tracking framework that integrates error mitigation techniques with quantum reservoir computing. Validated through a chaotic time series prediction case study, the framework enables fully reproducible tracking of quantum experiments, accurately identifies critical error sources, and aggregates marginal gains across the workflow. The approach offers a generalizable methodological foundation for advancing quantum software engineering practices.