Observing the Quantum Compiler through Automatic Experiment Tracking for Qiskit
- URL: http://arxiv.org/abs/2608.05041v1
- Date: Wed, 05 Aug 2026 16:51:15 GMT
- Title: Observing the Quantum Compiler through Automatic Experiment Tracking for Qiskit
- Abstract summary: This demonstration presents an MLflow-inspired autologging framework for Qiskit that automatically captures compiler provenance.<n>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.
- Score: 1.7188280334580195
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Catching Transpilation Drift with a CI/CD Workflow in Quantum Software Development [0.0]
This paper proposes a workflow for detecting transpilation drift before execution.<n>The contribution is a practical CI/CD guardrail for making quantum compilation behavior observable, testable, and auditable.
arXiv Detail & Related papers (2026-08-08T17:22:04Z) - TuniQ: Autotuning Compilation Passes for Quantum Workloads at Scale for Effectiveness and Efficiency [3.7546942901849225]
We present TuniQ, a reinforcement learning-based system that selects compilation passes at each pipeline stage.<n>TuniQ scales strongly to utility-scale circuits with growing advantage.
arXiv Detail & Related papers (2026-05-12T00:58:42Z) - Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
arXiv Detail & Related papers (2025-05-28T16:50:05Z) - Breaking Down Quantum Compilation: Profiling and Identifying Costly Passes [40.11095094521714]
We perform a preliminary analysis of the quantum circuit compilation process in Qiskit.<n>We identify the tasks that most strongly impact the overall compilation time.<n>Our results indicate that, as the desired level of optimization increases, circuit synthesis and gate passes become the dominant tasks.
arXiv Detail & Related papers (2025-04-21T14:45:01Z) - Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox [46.39670209441478]
Large language models (LLMs) have exhibited exciting progress in multiple scenarios.
As an effective means to reduce memory footprint and inference cost, quantization also faces challenges in performance degradation at low bit-widths.
This work provides a comprehensive benchmark suite for this research topic, including an evaluation system, detailed analyses, and a general toolbox.
arXiv Detail & Related papers (2024-06-15T12:02:14Z) - On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks [52.97107229149988]
We propose an On-Chip Hardware-Aware Quantization framework, performing hardware-aware mixed-precision quantization on deployed edge devices.
For efficiency metrics, we built an On-Chip Quantization Aware pipeline, which allows the quantization process to perceive the actual hardware efficiency of the quantization operator.
For accuracy metrics, we propose Mask-Guided Quantization Estimation technology to effectively estimate the accuracy impact of operators in the on-chip scenario.
arXiv Detail & Related papers (2023-09-05T04:39:34Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Quantum Circuit Transformations with a Multi-Level Intermediate
Representation Compiler [1.5855260762884629]
We present a novel adaptation of the multi-level intermediate representation (MLIR) integrated into a quantum compiler.
We first present how MLIR enables quantum circuit transformations for efficient execution on quantum computing devices.
We demonstrate that mirror circuits inserted during compilation may test hardware performance.
arXiv Detail & Related papers (2021-12-20T16:59:27Z) - CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
(Extended Version) [62.96267257163426]
We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework.
First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case.
Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
arXiv Detail & Related papers (2021-03-18T20:22:50Z) - Extending C++ for Heterogeneous Quantum-Classical Computing [56.782064931823015]
qcor is a language extension to C++ and compiler implementation that enables heterogeneous quantum-classical programming, compilation, and execution in a single-source context.
Our work provides a first-of-its-kind C++ compiler enabling high-level quantum kernel (function) expression in a quantum-language manner.
arXiv Detail & Related papers (2020-10-08T12:49:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.