quantum amplitude estimation and related algorithms

Addressing the challenges of heterogeneous resource scheduling, cross-paradigm data movement, and unified access to both NISQ and fault-tolerant quantum devices in deep quantum–HPC integration, this paper proposes the first hardware-agnostic quantum–supercomputing fusion software stack. Methodologically, it introduces (1) a quantum–classical unified resource management framework; (2) a hardware-abstracted programming interface and a cross-platform Quantum Platform Manager API; and (3) a quantum gateway supporting REST/gRPC, a hybrid scheduler, and a quantum-circuit co-optimization toolchain. The stack seamlessly integrates with mainstream HPC job schedulers (e.g., Slurm, PBS). Empirical evaluation on hybrid algorithms—including the variational quantum linear solver—on real supercomputing systems demonstrates significant improvements in quantum–classical resource utilization and task throughput. The design is both practically deployable and inherently scalable, establishing a foundational infrastructure for large-scale quantum–HPC convergence.

Quantum software and artificial intelligence development face significant challenges, including a scarcity of skilled personnel, low development efficiency, and complex deployment decisions in hybrid systems. This study presents the first systematic literature review on automated software engineering and AI methods specifically tailored for quantum and hybrid quantum-classical systems. It synthesizes existing techniques, tools, and application strategies, while identifying key automation approaches and critical research gaps. By addressing the lack of comprehensive reviews in this interdisciplinary domain, the work establishes a theoretical foundation and offers practical pathways to enhance the development efficiency and deployment intelligence of quantum–AI integrated systems.

Reproducibility, scalability, and cross-team collaboration in quantum computing experiments remain significant challenges. To address these, this work introduces a structured experimental tracking workflow—marking the first systematic adoption of the MLflow framework in quantum research. It enables unified, standardized logging of parameters, metrics, source code, and quantum circuits within hybrid classical-quantum experiments. The approach seamlessly integrates mainstream quantum programming frameworks—including Qiskit and PennyLane—and supports versioned experiment records and exact result reproducibility. Empirical evaluation demonstrates that the proposed methodology substantially improves reproducibility and collaborative efficiency in quantum software development, reduces knowledge retention overhead, and establishes a scalable engineering infrastructure for large-scale quantum algorithm development and interdisciplinary collaboration.

This work addresses the lack of interoperability among current quantum compilation tools, which hinders the development of a unified software stack. It presents the first systematic integration of PennyLane and the Munich Quantum Toolkit (MQT) through the MLIR framework, leveraging MLIR’s intermediate representation and LLVM infrastructure to establish reusable integration pathways and best practices. By lowering the barrier to entry for quantum software engineers adopting MLIR, this approach enables seamless interoperability between distinct quantum toolchains. The study provides both a practical roadmap and actionable strategies for constructing a modular, interoperable quantum software ecosystem, thereby advancing the standardization and composability of quantum programming tools.

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 the lack of systematic understanding regarding the challenges faced by quantum computing developers when using practical toolchains and algorithms. Through the first large-scale empirical analysis of 1,404 Stack Overflow posts, combining topic modeling, quantitative content analysis, and evaluation of answer acceptance rates and response times, the work reveals the topical landscape, tool adoption patterns, and algorithmic references within the developer community. Seven core themes are identified, with hybrid quantum-classical computing and quantum circuit implementation emerging as the most prominent. Qiskit and Q# dominate as the primary development frameworks, while Grover’s and Shor’s algorithms are the most frequently cited. The study further quantifies significant differences across topics in terms of problem-solving difficulty and levels of community support.

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.

Writing quantum programs is error-prone and lacks automated tools for precise analysis of their expected costs. This work proposes Qet, the first fully automatic static analysis tool based on the quantum expectation transformer framework, which derives upper bounds on the expected cost of hybrid classical-quantum programs featuring intermediate measurements and classical control flow. By generalizing Dijkstra’s predicate transformers and Hoare logic and integrating them with static analysis techniques, Qet automatically computes tight upper bounds that previously required tedious manual derivations. The approach operates without human intervention and has been validated on multiple benchmark programs, demonstrating both its effectiveness and state-of-the-art performance.

This work addresses the limitations of existing quantum resource estimation methods, which rely heavily on compilation pipelines or expert knowledge and are tightly coupled to long-term fault-tolerant assumptions, rendering them ill-suited for the diverse demands of current hardware–algorithm co-design. To overcome these challenges, the paper proposes an automated, full-stack resource estimation and optimization framework. It features a user-definable abstraction of the quantum computing stack, a modular component library, hierarchical resource surrogate models built upon algorithmic profiling and neurosymbolic learning, and a multi-objective optimization mechanism embedded within the deployment workflow. Evaluated across scenarios including fault-tolerant algorithms, small-scale error-correcting codes, gate decompositions, and variational circuit training, the framework uncovers nontrivial resource trade-offs overlooked by existing tools, demonstrating its generality and practical utility.

This study addresses the lack of systematic empirical analysis on how quantum software patterns are combined and sourced in real-world projects. To bridge this gap, the authors propose a novel pattern detection approach that integrates multi-channel matching with lexical expansion, constructing a traceable component call graph stored in a graph database. By combining static analysis, natural language processing, and large language model (LLM)-enhanced classification, their method enables fine-grained mining of pattern co-occurrence relationships and direct invocation ratios for the first time. Evaluated on the Qrisp framework, the approach achieves a micro F1-score of 0.712—an improvement of 63%—and validates the practical usage of all 23 known quantum patterns across 80 real projects. The work also contributes an open-source quantum pattern analysis pipeline (qpa) with 286 component mappings and a cross-framework dataset capturing pattern usage across five quantum software ecosystems.