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.
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.
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.
Quantum generative models (QGMs), including Variational Quantum Circuits (VQCs) and Quantum GANs, hold significant potential in generating complex data distributions beyond the capabilities of classical generative approaches. However, robust monitoring and evaluation of QGMs remain underdeveloped due to hardware constraints, stochastic quantum behavior, and reproducibility limitations. This paper proposes a scalable and modular framework using Apache Spark and MLflow to monitor, evaluate, and track the performance of QGMs. The framework enables ingestion of quantum-generated data, distributed computation of performance metrics such as fidelity, entanglement entropy, distributional divergence, and experiment tracking via MLflow. I validate our methodology using Qiskit-based simulated QGMs and demonstrate the effectiveness of classical big data tools in bridging the evaluation gap in quantum ML research.
Abstract Quantum computing promises breakthroughs in various application areas, such as machine learning, chemistry, or simulations. However, today’s quantum computers are error‐prone and have limited capabilities. This leads to various challenges when developing and executing quantum algorithms, for example, the mitigation of occurring errors or the selection of a suitable quantum computer to execute a certain quantum circuit. To address these challenges, detailed information about the quantum circuit to be executed as well as past executions, and the up‐to‐date information about the available quantum computers are required. Thus, this data must be continuously collected and stored in the long‐term, which is currently not supported. To overcome this problem, a provenance approach is introduced for quantum computing. Therefore, relevant provenance attributes that should be gathered in the area of quantum computing are identified. Furthermore, QProv, a provenance system that automatically collects the identified provenance attributes and provides them in a uniform manner to the user is introduced. Finally, a case study with the collected provenance data and corresponding use cases that can benefit from this provenance data are presented here.
Quantum compiler optimization is important for improving the execution fidelity of quantum circuits. In practice, this optimization is often formulated as reducing circuit-level metrics. In classical compilers, pass tuning has been well-established through various methods. However, these methods cannot be directly applied to quantum compilers, because quantum programs exhibit quantum-specific features. Existing quantum compiler tuning studies have attempted to incorporate quantum circuit features with compiler tuning techniques, but they still suffer from two key limitations. First, they tune only a limited subset of optimization passes, leaving the global effectiveness of the full optimization pass space underexplored. Second, the quantum features they use are mainly static features, such as structure and DAG features, which are not directly tied to the optimization characteristics of quantum circuits.
In this paper, we propose QTuner, a feature-guided framework for quantum compiler pass tuning. We first construct an optimization dataset by performing Bayesian Optimization and recording their optimized pass sequences. Based on this dataset, QTuner represents each circuit with two types of features: static circuit features and optimization-aware pass embeddings. Finally, QTuner trains two models offline, which are used to efficiently retrieve and rank promising pass sequences for a new circuit. We evaluate QTuner on two benchmarks across Qiskit and PyTKET compilers. On Qiskit, QTuner achieves up to a 10.6-percentage-point improvement in 2Q gate reduction over the strongest baseline while reducing tuning time by 73.59%. On PyTKET, QTuner achieves up to a 9.4-percentage-point improvement in 2Q gate reduction and reduces tuning time by 64.49%. These results demonstrate that QTuner enables efficient pass tuning across quantum compilers.
Quantum compilers play a critical role in transforming high-level quantum programs into optimized, hardware-compatible circuits. However, verifying the correctness of compiler passes remains challenging, as determining the expected output of large, deeply entangled quantum circuits is computationally intractable. This challenge is further amplified when compiler passes modify already complex circuit structures, making manual validation of transformed circuits impractical.
In this work, we perform a systematic analysis of unit tests for quantum compiler passes in four quantum programming frameworks (PennyLane, Qiskit, Cirq, and pytket). Our findings indicate validation is dominated by program-content and program-metric assertions, and test circuits are generally small and shallow. Motivated by these observations, we introduce a testing methodology for automated validation of quantum compiler passes based on retromorphic testing and principles from the Hadamard test. This methodology analyzes a compiler pass, test circuit, and expected pass behavior to verify semantic preservation and intended structural modifications.
We implement our methods in a framework, RetroQ, and apply it to compiler passes in PennyLane and Qiskit. Experimental evaluation reproduced several existing bugs as well as uncovered previously undetected defects, such as flawed symbolic parameter handling, incorrect commutation logic, failure to recognize self-adjointness of gates, and runtime crashes. These findings highlight the need for compiler-pass-specific testing methodologies to improve the reliability of the evolving quantum software stack.
Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repository-ready form. In the NISQ era, probabilistic outputs, time-varying hardware conditions, and opaque transpilation pipelines create a data-management problem that directly affects reproducibility, traceability, and long-term reuse of experimental records. Goal: This paper aims to address this gap by proposing a specialized metadata and schema model for managing quantum-circuit execution data as governed, machine-interpretable, and evolvable repository artifacts. Proposal: We propose QC-MM, a platform-agnostic metadata model for capturing, validating, and relating contextual evidence of quantum-circuit experiments. The model integrates time-indexed calibration binding, transpilation traceability, lightweight provenance links, validation rules, and controlled schema evolution through a JSON Schema specification. Results: The evaluation follows a multi-scenario protocol and shows that QC-MM captures dynamic calibration context in IBM Quantum Cloud, remains interoperable through a local SpinQ NMR device, and makes transpilation effects traceable through structured records. It also supports repeated-run statistical reporting and links compilation decisions to execution outcomes, including circuit-depth reductions and changes in an estimated fidelity proxy under different optimization settings. Conclusions: QC-MM provides a specialized data-modeling and schema-governance foundation for traceable quantum-experiment repositories. Beyond improving reproducibility-oriented reporting, the proposal contributes to metadata validation, controlled schema evolution, and repository-oriented management of contextual experimental data.