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Toward Live Noise Fingerprinting in Quantum Software Engineering

Avner Bensoussan, Elena Chachkarova, Karine Even-Mendoza, Sophie Fortz, Vasileios Klimis

TL;DR

The paper tackles the challenge of undocumented noise-model differences across quantum software platforms by proposing live empirical noise fingerprinting. It introduces SimShadow, a shadow-tomography-inspired protocol that generates a compact deviation fingerprint to characterize simulator noise and enable cross-platform validation, noise-aware compilation, and debugging. Empirical results show distinct, platform-specific fingerprint patterns with substantial Frobenius distances between Qiskit and Cirq, and a dramatic reduction in measurement requirements compared with full tomography, enabling scalable QSE tooling. The work lays a foundation for standardized benchmarking, hardware-informed simulation, and educational tools to improve portability, reproducibility, and developer reasoning in quantum software engineering.

Abstract

Noise is a major bottleneck in today's quantum computing, stemming from decoherence, gate imperfections and other hardware limitations. Accurate noise fingerprints are essential, yet undocumented noise model differences between Quantum Ecosystems undermine core functionality, such as compilation, development and debugging, offering limited transferability and support for quantum software engineering (QSE) tasks. We propose a new research direction: live empirical noise fingerprinting as a lightweight QSE-oriented "noise fingerprinting". Though explored in physics as device-level diagnostics, we reposition them as a QSE paradigm: we propose leveraging classical shadow tomography to enable a new generation of techniques. As a first step, we introduce SimShadow, which prepares reference states, applies shadow-tomography-inspired estimation and constructs deviation fingerprints. Initial experiments uncover systematic discrepancies between platforms (e.g. Frobenius distances up to 7.39) at up to 2.5x10^6 lower cost than traditional methods. SimShadow opens new directions for noise-aware compilation, transpilation, cross-platform validation, error mitigation, and formal methods in QSE.

Toward Live Noise Fingerprinting in Quantum Software Engineering

TL;DR

The paper tackles the challenge of undocumented noise-model differences across quantum software platforms by proposing live empirical noise fingerprinting. It introduces SimShadow, a shadow-tomography-inspired protocol that generates a compact deviation fingerprint to characterize simulator noise and enable cross-platform validation, noise-aware compilation, and debugging. Empirical results show distinct, platform-specific fingerprint patterns with substantial Frobenius distances between Qiskit and Cirq, and a dramatic reduction in measurement requirements compared with full tomography, enabling scalable QSE tooling. The work lays a foundation for standardized benchmarking, hardware-informed simulation, and educational tools to improve portability, reproducibility, and developer reasoning in quantum software engineering.

Abstract

Noise is a major bottleneck in today's quantum computing, stemming from decoherence, gate imperfections and other hardware limitations. Accurate noise fingerprints are essential, yet undocumented noise model differences between Quantum Ecosystems undermine core functionality, such as compilation, development and debugging, offering limited transferability and support for quantum software engineering (QSE) tasks. We propose a new research direction: live empirical noise fingerprinting as a lightweight QSE-oriented "noise fingerprinting". Though explored in physics as device-level diagnostics, we reposition them as a QSE paradigm: we propose leveraging classical shadow tomography to enable a new generation of techniques. As a first step, we introduce SimShadow, which prepares reference states, applies shadow-tomography-inspired estimation and constructs deviation fingerprints. Initial experiments uncover systematic discrepancies between platforms (e.g. Frobenius distances up to 7.39) at up to 2.5x10^6 lower cost than traditional methods. SimShadow opens new directions for noise-aware compilation, transpilation, cross-platform validation, error mitigation, and formal methods in QSE.

Paper Structure

This paper contains 8 sections, 1 equation, 3 figures.

Table of Contents

  1. Introduction
  2. Live Empirical Noise Fingerprinting
  3. The Noise Fingerprinting Paradigm
  4. The SimShadow Protocol
  5. Emerging Findings
  6. Experimental Setup
  7. Emerging Results
  8. Research Agenda for QSE

Figures (3)

  • Figure 1: SimShadow Framework Architecture: Standardised reference states are run on a noisy simulator. A shadow-tomography-inspired protocol is used to efficiently estimate properties of the output, which are then compared to ideal values to generate a unique noise fingerprint.
  • Figure 2: (Result 1) Fingerprint Visualisation & Cross Platform Difference. Heatmaps of the fingerprint matrix $F$ for depolarising, amplitude damping, and phase damping in Qiskit (top row), Cirq (middle row) and the difference matrix $\Delta = F_{\text{Qiskit}} - F_{\text{Cirq}}$ between platforms (bottom row). The Frobenius distance (a single scalar value) is computed from this difference matrix as $\|\Delta\|_F$ and quantifies the total magnitude of cross-platform differences.
  • Figure 3: (Result 2) Scalability Advantage. Measurement requirements for SimShadow vs traditional process tomography.