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Evaluating Calibration-Based Digital Twins for IBM Quantum Hardware Simulation

Edgars Bautra, Maksims Dimitrijevs, Abuzer Yakaryilmaz

Abstract

We evaluate calibration-based digital twins for IBM Quantum hardware, aiming to reproduce hardware measurement outcomes on classical simulators. We present a workflow that builds twins from downloadable calibration CSV files by mapping coherence times, gate and readout error rates, and operation durations to thermal-relaxation, depolarizing, and readout error channels, while reconstructing a directed coupling map to restore connectivity constraints during transpilation. We compare four twin variants (CSV-built, backend-derived simulator, backend-derived noise model, and fake-backend snapshots) under a common execution and validation protocol. Experiments on two IBM QPUs, ibm_brisbane and ibm_sherbrooke, use randomized five-qubit circuits of depths 10, 20, and 30 across four optimization levels. Weighted Jaccard similarity indicates that twins constructed from downloadable calibration CSV data often achieved the closest agreement with hardware, while backend-derived twins provided competitive and practical baselines. The results further show that agreement depends on both the target device and the transpilation settings, underscoring the need to validate digital twins for the specific execution setup rather than assuming transferability across devices.

Evaluating Calibration-Based Digital Twins for IBM Quantum Hardware Simulation

Abstract

We evaluate calibration-based digital twins for IBM Quantum hardware, aiming to reproduce hardware measurement outcomes on classical simulators. We present a workflow that builds twins from downloadable calibration CSV files by mapping coherence times, gate and readout error rates, and operation durations to thermal-relaxation, depolarizing, and readout error channels, while reconstructing a directed coupling map to restore connectivity constraints during transpilation. We compare four twin variants (CSV-built, backend-derived simulator, backend-derived noise model, and fake-backend snapshots) under a common execution and validation protocol. Experiments on two IBM QPUs, ibm_brisbane and ibm_sherbrooke, use randomized five-qubit circuits of depths 10, 20, and 30 across four optimization levels. Weighted Jaccard similarity indicates that twins constructed from downloadable calibration CSV data often achieved the closest agreement with hardware, while backend-derived twins provided competitive and practical baselines. The results further show that agreement depends on both the target device and the transpilation settings, underscoring the need to validate digital twins for the specific execution setup rather than assuming transferability across devices.
Paper Structure (24 sections, 3 equations, 1 figure, 3 tables)

This paper contains 24 sections, 3 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Main Contributions
  3. Technical Details
  4. Problem setting and digital twin definition
  5. Validation Goal and Acceptance Criteria
  6. Twin Variants and Execution Workflow
  7. Calibration Data Extraction and Preprocessing
  8. Noise Model Construction from Calibration Data
  9. Coupling Map Reconstruction from Calibration Tables
  10. Backend- and Snapshot-Based Noise Models
  11. Simulator Configuration Choices
  12. Evaluation Protocol
  13. Similarity Metric
  14. Benchmark Circuits and Transpilation Settings
  15. Shot Budget Selection
  16. ...and 9 more sections

Figures (1)

  • Figure 1: Generated five-qubit quantum circuit with depth 10