Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated.
In this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints' ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs' behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost. On 69 MQTBENCH programs, larger fingerprint differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.
Contemporary quantum computers are inherently noisy, posing significant challenges for the development and testing of quantum software. Simplified or outdated noise assumptions can lead to incorrect assessments of program correctness, obscure debugging, and hinder cross-platform portability, creating a critical quantum software development gap. Providing accurate, practical noise characterisation is challenging as traditional reconstruction methods scale exponentially and rapidly become outdated.
In this vision paper, we address this gap via a novel classical shadow tomography-based pipeline, SIMSHADOW, enabling efficient, continuously updatable noise fingerprinting from empirical observations, suitable for integration into quantum software development workflows, including testing and validation. We prototyped the pipeline to investigate fingerprints' ability to capture structured, interpretable noise and cross-platform discrepancies affecting quantum programs' behaviour to support realistic testing and debugging in future tools. Our evaluation with Qiskit and Cirq under widely used hardware-informed profiles, IBM Boston and Quantinuum H2, shows fingerprints exhibit channel-specific structure and yield interpretable heatmaps. We observed systematic cross-platform discrepancies under matched noise configurations, quantified by large Frobenius distances at a fraction of full tomography cost. On 69 MQTBENCH programs, larger fingerprint differences correlate with output distributions divergences, highlighting threats for testing and cross-platform debugging tasks.
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
Quantum computing in the Noisy IntermediateScale Quantum (NISQ) era presents significant challenges in differentiating quantum software bugs from hardware noise. Traditional debugging techniques from classical software engineering cannot directly resolve this issue due to the inherently stochastic nature of quantum computation mixed with noises from NISQ computers. To address this gap, we propose a statistical approach leveraging probabilistic metrics to differentiate between quantum software bugs and hardware noise. We evaluate our methodology empirically using well-known quantum algorithms, including Grover's algorithm, Deutsch-Jozsa algorithm, and Simon's algorithm. Experimental results demonstrate the efficacy and practical applicability of our approach, providing quantum software developers with a reliable analytical tool to identify and classify unexpected behavior in quantum programs.
A Suite for Testing and Debugging Quantum Programs", addresses an underdeveloped area of research in the current scope of quantum computing: testing and debugging quantum programs.This work makes a significant contribution not only to the future of quantum software development but also more broadly to the advancement of the field of quantum computing, as software tools are critical to maturing from the current NISQ (noisy intermediate-scale quantum) era to the FTQC (fault-tolerant quantum computation) era.Quantum technology has been rapidly evolving, as seen through the significant advances in quantum computing, sensing, cryptography, and communication.Quantum technology is transitioning from experimental prototypes to more practical, yet still limited, applications.These advances offer the prospect of unprecedented computational capabilities for specific tasks like optimization, chemical simulation, and cryptography but are hindered by challenges such as error rates, qubit coherence times, and scalability.All of these problems are being addressed by physicists and engineers.One area receiving far too little attention is the emerging field of quantum software engineering.
The recent surge in interest in quantum computing has led to the emergence of quantum-native software development: a multidisciplinary field that combines quantum information theory, classical software engineering, and system-level orchestration. Hybrid quantum-classical systems, particularly noisy intermediate-scale quantum (NISQ) hardware, which is of interest in the short term, demand interoperability between quantum computation hardware and classical computing resources. The paper takes a broad look at the entire scenario of quantum-native development, in particular, in terms of tooling, design patterns, and debugging paradigms. We conduct a comprehensive survey, including the examination of compiler platforms, SDKs, debugging tools, and simulators. Their new tendencies include quantum workflow orchestration, hybrid execution graphs, circuit reuse, and noise-aware algorithm design, all of which are covered in detail. We also examine the difficulty of debugging with two paradigms: quantum mechanics and classical determinism. Through comparative studies of current frameworks, such as Qiskit, Cirq, and PennyLane, and practical tests of benchmarks, including VQE and QAOA, we will provide guidance to developers, researchers, and system architects working on quantum computers. The article proposes a taxonomy of tools as layered, a pattern repository, and a framework for a debugging pipeline to facilitate future work in both research and implementation. The conceptual contributions are presented in context through tables, flowcharts, and visual references. Overall, the purpose of this paper is to take a step towards the process of quantum software engineering, which would enable the developer community to explore a fresh landscape of quantum-native development
Noisy Intermediate-Scale Quantum (NISQ) hardware has transformed quantum computing into a practical experimental platform while introducing fundamental challenges associated with hardware noise, decoherence, and probabilistic execution. Although quantum software testing (QST) has rapidly emerged as an active research area, it remains unclear to what extent existing testing methodologies explicitly engage with noisy and hardware-constrained execution environments. This paper presents a systematic empirical landscape analysis of noise-awareness in QST research through a deterministic, taxonomy-guided analytical framework applied to 61 papers published between 2018 and 2025 across eight testing paradigms, contrasting how contributions are framed with how they are evaluated. Among the analyzed papers, 47 (77.0%) are noise-oblivious and only 14 (23.0%) explicitly engage with noise-related execution concerns, while only 11 (18.0%) report evaluation on physical hardware or a noisy simulator. Test generation, the largest paradigm, exhibits a noise-awareness rate of only 14.3%, and debugging and repair 18.2%; coverage-based, metamorphic, and differential testing contain no noise-aware contributions. Noise-aware methodologies emerged late and remain outnumbered in all analyzed years. Framing and evaluation are strongly associated (odds ratio 40.5, p < 0.001) yet diverge for individual papers: 5 of the 14 noise-aware papers evaluate only on ideal simulators. Inferential statistical tests are reported in only 16.4% of papers. The classification framework was validated against a blinded, human-coded reference sample, achieving almost perfect inter-rater agreement on testing-technique classification and substantial agreement on noise-awareness. The framework provides a reusable empirical basis for longitudinal analysis of hardware-conscious testing practice.
Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms. As real quantum hardware is still limited in size and accessibility, noise models for classical simulation-that in some cases can exceed dimensions of actual systems-play a critical role in obtaining insights into quantum algorithm performance, and the properties of mechanisms for error correction and mitigation. We present, implement and validate a tunable noise model building on the Kraus channel formalism on a large scale quantum simulator system (Qaptiva). We use empirical noise measurements from IBM quantum (IBMQ) systems to calibrate the model and create a realistic simulation environment. Experimental evaluation of our approach with Greenberger-Horne-Zeilinger (GHZ) state preparation and QAOA applied to an industrial usecase validate our approach, and demonstrate accurate simulation of hardware behaviour at reasonable computational cost. We devise and utilise a method that allows for determining the quality of noise models for larger problem instances than is possible with existing metrics in the literature. To identify potentials of future quantum software and algorithms, we extrapolate the noise model to future partially fault-tolerant systems, and give insights into the interplay between hardware-specific noise modelling and hardware-aware algorithm development.