Artifact of Live Noise Fingerprints as an Empirical Semantics for Noisy Quantum Channels
Abstract Quantum computations execute on noisy hardware whose effective noise is opaque and varies across platforms and over time. Complete descriptions of the underlying noise channel are often too detailed for reasoning, comparison, or analysis. We therefore study the semantic problem of constructing tractable abstractions of noisy quantum channels: given a finite set of observations, how can we summarise channel behaviour while preserving properties of interest? We characterise the effective noise of a quantum channel as an abstract domain of noise fingerprints. A noise fingerprint is an observational abstraction of a quantum channel over a finite experiment interface. We formalise the relation between concrete channels and fingerprints through an approximate Galois connection and derive four abstraction properties: soundness, conciseness, efficiency, and adaptiveness. We provide an executable instantiation of the abstraction function using the classical-shadow estimator, together with a partial concretisation function that reconstructs channels from fingerprints via semidefinite programming. We evaluate this abstraction against the formal properties: the soundness bound is shown to be tight, compression over the concrete domain is exponential with near-lossless retention, sample complexity is (poly)logarithmic, and incremental updates track channel drift at the optimal rate. Additional experiments show that fingerprints preserve physically meaningful noise signatures and distinguish cross-platform behaviours. Together, this work brings abstract interpretation to the empirical semantics of noisy quantum channels, providing a lightweight, formally grounded abstraction for characterising, comparing, and monitoring noisy quantum executions. Context Characterising noisy quantum channels remains challenging: full process tomography scales exponentially with system size, while commonly used noise metrics provide limited semantic guarantees. This artifact accompanies a paper that introduces live noise fingerprints as a reusable semantic abstraction for noisy quantum channels. A noise fingerprint is defined as the matrix of deviations between observed and ideal Pauli expectation values over a fixed experimental interface. We show that these fingerprints form an abstract domain for completely positive trace-preserving (CPTP) maps through an approximate Galois connection to the concrete channel space. The framework is evaluated against four formal requirements: Soundness — a Lipschitz bound relating abstract distance to diamond-norm distance. Conciseness — substantial compression relative to the underlying channel representation. Estimation efficiency — practical estimation from finite measurements. Adaptiveness — support for incremental updates under channel drift. The concrete instantiation uses the Huang–Kueng–Preskill classical-shadow estimator with median-of-means aggregation. Every theoretical claim is machine-verified. The artifact includes exact SDP-based diamond norms for small systems, two independently validated large-system ground truths (completely bounded norm multiplicativity for single-site channel pairs and the exact Pauli-channel ℓ₁ formula), and a product-factorised classical-shadow sampler proven distribution-identical to the full sampler. All four research questions are evaluated for n = 1–8 qubits across 12 experimental configurations, combining three channel families with hardware-informed profiles derived from IBM Boston and Quantinuum H2 calibrations under both Qiskit and Cirq simulation variants. All stochastic measurements are reported as mean ± standard deviation, and every figure includes error bars. New in this version. The artifact now includes a self-contained worked example, worked_example.ipynb, which runs the abstraction end to end on small quantum programs: from OpenQASM source, through the gates and the noise a backend inserts around them, to the observation interface a program's test defines, the resulting fingerprints, and the decision they support. It is written for readers approaching the work from programming languages and software engineering rather than quantum information, and it is the shortest path into the artifact: it requires only NumPy, runs in seconds, and needs no SDP solver, quantum SDK, or hardware. The artifact also characterises the operational limits of the approach, including detectability thresholds at realistic hardware noise rates, readout-error-induced bias floors, explicit interface-blindness constructions and their informationally complete remedies, and drift regimes where warm-started updates remain beneficial. Artifact Evaluation file This artifact accompanies the paper "Live Noise Fingerprints as an Empirical Semantics for Noisy Quantum Channels" and provides the complete implementation, experiments, and evaluation pipeline used to validate the proposed noise-fingerprint abstraction. The package includes a pure Python/NumPy implementation of noisy quantum channels, classical-shadow fingerprint estimation, semidefinite-programming-based concretisation, machine-checked tests, a worked-example notebook, and scripts to reproduce every figure and quantitative claim reported in the paper. Evaluators can start from the notebook for a program-level walkthrough of the abstraction, verify correctness using an automated verification target, and regenerate all experimental results, including scalability, convergence, variance, detectability, and warm-start analyses. Precomputed datasets and figures are included to enable immediate inspection, while the full experimental pipeline can be rerun from scratch without requiring GPUs, quantum hardware, or network access after dependency installation. The artifact is released under the MIT License and is designed for reuse by allowing new quantum programs, hardware noise profiles, qubit counts, and abstraction properties to be incorporated with minimal modification. Package Contents worked_example.ipynb — a self-contained, executed walkthrough of the abstraction on small programs. It covers: an OpenQASM front end and the ideal denotation of a program; the realisation of that program on a backend, with the backend's noise inserted after every gate and rendered in the printed circuit; the observation interface, derived from what a program's test asserts rather than chosen freely; fingerprints and the equivalence decision, over five benchmark programs and five backend configurations, with a diagnostic per program naming the observation that carries the difference; interface blindness and its repair by refinement; prefix sweeping to localise which gate two realisations first disagree on; a control panel for running the whole chain on the reader's own .qasm file; measurement-reuse accounting, and numerical checks of three properties from the paper. Outputs are stored in the notebook, so it can be read without execution. src/ Pure NumPy implementation of: Quantum states and channels Noise fingerprints Literal classical-shadow estimators (full and product-factorised) Optional readout-error models Exact diamond-norm computation via Watrous SDPs Validated large-system diamond-norm ground truths General SDP-based concretisation procedures Configurable noise models Registry of all 12 experimental configurations (profiles.py) experiments/ Experiment drivers for all research questions: rq1_sweep.py — Soundness rq2_sweep.py — Conciseness rq3_sweep.py — Estimation Efficiency rq4_sweep.py — Adaptiveness Additional components include audited single-size reproductions, a stress-test suite (realistic_limits.py), a seed-sweep audit framework, and a figure-generation pipeline with shared publication styling. results/ — JSON outputs containing every measured data point for each research question. figures/ — Results figures, one file per figure. tests/ — Forty-one machine-checked tests, including validation of exact diamond-norm ground truths, verification of the product-factorised sampler, and consistency checks required before experimental execution. Additional files — README.md (overview, results summary, and reproduction guide), Dockerfile (containerised environment), requirements.txt (pinned dependencies). Installation The artifact requires Python 3.10 or later. Install dependencies: pip install -r requirements.txt Required packages: NumPy, SciPy, CVXPY, scikit-learn, matplotlib. Opening the worked-example notebook additionally requires jupyter; the notebook itself imports only NumPy and can also be read as-is, since its outputs are stored. No quantum SDK is required; all simulations use a pure NumPy density-matrix implementation. A containerised environment is also provided: docker build . Getting started: the worked example jupyter notebook notebooks/worked_example.ipynb Approximate runtime: under one minute; NumPy only. This is the recommended entry point. It takes a program in OpenQASM, shows what a backend does to it, derives the observation interface from the program's test, computes the fingerprints, and decides whether the two backends are interchangeable for that test — the abstraction of the paper, on objects small enough to check by hand. Expected results Two backends implementing the same physical noise by different means are judged interchangeable on all five programs, with deviations at machine precision (≈ 10⁻¹⁶). A leftover coherent phase rotation, of the kind an optimisation pass can introduce, is flagged on four of the five programs and correctly cleared on the fifth, whose test reads only populations and therefore cannot be affected by it. Judged on a population-only interface, the same phase rotation is invisible; refining the interface to the observables the program's test actually reads restores it. The prefix sweep localises a fault confined to the two-qubit gate at exactly that gate, and reports the earliest divergence when the