Quantum software workflows rely on compiler and provider toolchains that evolve independently of application source code. Consequently, an unchanged quantum circuit may transpile into a different target-specific realization after changes in SDK versions, optimization settings, basis gates, coupling maps, or backend descriptions. Such transpilation drift can affect circuit depth, gate composition, qubit mapping, and execution behavior, yet it is rarely monitored in CI/CD pipelines. This paper proposes a Quantum DevOps workflow for detecting transpilation drift before execution. The workflow transpiles source circuits against configured target profiles, computes structural drift metrics, records provenance and artifacts in MLflow, and raises configurable warnings or failures in GitHub Actions. Using representative circuits and target profiles, we show how drift checks can expose toolchain-induced changes and support reproducibility audits. The contribution is a practical CI/CD guardrail for making quantum compilation behavior observable, testable, and auditable.
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Accepted for publication in the Q-SET Workshop at IEEE QCE 2026
Quantum software workflows rely on compiler and provider toolchains that evolve independently of application source code. Consequently, an unchanged quantum circuit may transpile into a different target-specific realization after changes in SDK versions, optimization settings, basis gates, coupling maps, or backend descriptions. Such transpilation drift can affect circuit depth, gate composition, qubit mapping, and execution behavior, yet it is rarely monitored in CI/CD pipelines. This paper proposes a Quantum DevOps workflow for detecting transpilation drift before execution. The workflow transpiles source circuits against configured target profiles, computes structural drift metrics, records provenance and artifacts in MLflow, and raises configurable warnings or failures in GitHub Actions. Using representative circuits and target profiles, we show how drift checks can expose toolchain-induced changes and support reproducibility audits. The contribution is a practical CI/CD guardrail for making quantum compilation behavior observable, testable, and auditable.
Understanding the effectiveness of quantum compilation techniques requires visibility into the entire transpilation process, not just the final circuit metrics. This demonstration presents an MLflow-inspired autologging framework for Qiskit that automatically captures compiler provenance, including transpilation stages, pass-level execution data, backend characteristics, compiler configuration, and execution results. The framework extends the QProv provenance model with compiler-specific information and stores the collected data in an MLflow Tracking Server for analysis and visualization. By eliminating manual instrumentation, the proposed approach improves compiler observability and supports reproducible evaluation of quantum compilation workflows.
Abstract Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many open-source quantum programs are affected by quantum-specific code smells, with long circuit being the most common. While the study provided relevant insights into the prevalence of code smells in quantum circuits, it did not explore the potential effect of transpilation, a necessary step for executing quantum computer programs, on the emergence of code smells. Indeed, transpilation might alter those characteristics employed to detect the presence of a smell on a circuit. To address this limitation, we present a new study investigating the impact of transpilation on quantum-specific code smells and how different target gate sets affect the results. We conducted experiments on 17 open-source quantum programs alongside a set of 100 synthetic circuits. We found that transpilation can significantly alter the metrics that are used to detect code smells, even into previously smell-free circuits, with the long circuit smell being the most susceptible to transpilation. Furthermore, the choice of the gate set significantly influences the presence and severity of code smells in transpiled circuits, highlighting the need for careful gate set selection to mitigate their impact. These findings have implications for circuit optimization and high-quality quantum software development. Further research is needed to understand the consequences of code smells and their potential impact on quantum computations, considering the characteristics and constraints of different gate sets and hardware platforms.
This preprint documents a reproducible failure mode in cross-platform quantum benchmarking: compiler/transpiler optimization can silently invalidate circuit-depth (or “barrier-based”) proxies for decoherence exposure by eliminating the intended experimental variable. We show how barrier or idle-depth manipulation may collapse after compilation, producing misleading null depth-dependence or inflated conclusions. We provide an audit workflow based on compiled-circuit metadata (depth, 1Q/2Q gate counts, and optimization settings) and recommend benchmark designs that encode the experimental variable in gate parameters or analysis steps (e.g., phase-sweep parity witnesses), which are robust to structural circuit simplification. Related dataset: Cross-platform GHZ benchmarking JSON logs and analysis artifacts are available on Zenodo (concept DOI: 10.5281/zenodo.18530246; current version: 10.5281/zenodo.18530247). This methodological note is a companion focused on compiler/transpilation artefacts and reporting standards for compiled-circuit audits.
Parameterized circuit instantiation is a common technique encountered in the generation of circuits for a large class of hybrid quantum-classical algorithms. Despite being supported by popular quantum compilation infrastructures such as IBM Qiskit and Google Cirq, instantiation has not been extensively considered in the context of circuit compilation and optimization pipelines. In this work, we describe algorithms to apply instantiation during two common compilation steps: circuit optimization and gate-set transpilation. When placed in a compilation workflow, our circuit optimization algorithm produces circuits with an average of 13% fewer gates than other optimizing compilers. Our gate-set transpilation algorithm can target any gate-set, even sets with multiple two-qubit gates, and produces circuits with an average of 12% fewer two-qubit gates than other compilers. Overall, we show how instantiation can be incorporated into a compiler workflow to improve circuit quality and enhance portability, all while maintaining a reasonably low compile time overhead.
The performance constraints of conventional DevOps and CI/CD pipelines have been exceeded by the increasing complexity of software systems and the desire for fast, reliable releases. Important DevOps activities including optimising builds, scheduling tests, resolving dependencies, and allocating resources are being investigated as possible accelerators by emerging quantum computing and quantum-inspired approaches. With a particular emphasis on speeding up CI/CD processes, this comprehensive overview analyses recent studies that have investigated the use of quantum and quantum-inspired methods in DevOps. Findings from recent white papers, conference proceedings, and peer-reviewed journals are included in this review. It classifies previous work into primary domains such as quantum optimisation for pipeline scheduling, quantum annealing for combinatorial testing, hybrid classical-quantum models for deployment choices, and quantum-inspired heuristics usable on classical hardware. Most methods are still experimental or hybrid, and the research notes that although some have shown performance benefits, others have practical limits and are not yet ready. Also covered are important issues like skills shortages, algorithm scalability, hardware accessibility, and interface with current DevOps toolchains. Although completely quantum CI/CD pipelines are still in the future, the research finds that quantum-inspired methodologies may increase pipeline efficiency and decision-making in the near term, providing a feasible means to accelerate next-generation DevOps.
Quantum compilers face the test oracle problem, judged by an output-equivalence oracle: the compiled circuit must compute the same unitary as the original, modulo global phase and a qubit-layout permutation. This oracle, by construction, checks only that semantic map, not the circuit's own layout, permutation, or phase records: a defect there, or in a fixed-seed run's determinism, can pass unseen though the record is public. This empirical software engineering study of quantum transpiler correctness uses repository mining to measure how often this happens in real merged compiler fixes: a systematically identified corpus of Qiskit transpiler bug-fixes, classified by an independently dual-coded, source-validated fault-manifestation taxonomy. Nineteen of 68 fixes (28%, 95% Wilson CI 19-40%) repair faults invisible to this equivalence screen, even one augmented with compilation-validity, circuit-quality, and performance checks, and an extended 104-fix corpus over a wider window holds at the same rate with a tighter interval (29/104, 27.9%, CI 20-37%). A conservative floor remains even restricted to the one unconditionally equivalence-invisible channel (a corrupted layout or permutation record): 10 of 68 fixes (15%, CI 8-25%) beneath the 28% headline. The gap is not Qiskit-specific: it replicates in tket (7/21, 33%), with Cirq smaller but consistent. We detected no systematic differences on five inexpensive PR-level characteristics (19 vs 49, underpowered alone; the extended 29-vs-75 corpus tightens every interval toward zero). This class dominates the invisible set, concentrating at representation-boundary crossings. We release the corpus, codebook, and coding artifacts for quantum software testing research. Here we only measure it.