Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?

2025-07-01

AI Summary

Adaptive circuit translation in quantum computing—frequently relying on the latest noisy calibration data—does not necessarily improve circuit performance; instead, it exacerbates qubit load imbalance and error fluctuations. Method: We conduct a large-scale empirical study across five IBM 127-qubit processors, evaluating 16 quantum algorithms. Contribution/Results: First, we identify diminishing marginal returns of noise-aware translation. Second, we propose a randomized qubit mapping strategy that significantly reduces output error variance. Third, we empirically validate that circuits compiled once remain stable in fidelity across multiple calibration cycles, enabling cross-cycle reuse. Our results demonstrate that simplified translation strategies maintain average fidelity while drastically reducing classical compilation overhead—offering a new paradigm for efficient, scalable quantum computing workflows.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our findings reveal novel and interesting insights: (1) noise-aware transpilation leads to a heavy concentration of workloads on a small subset of qubits, which increases output error variability; (2) using random mapping can mitigate this effect while maintaining comparable average fidelity; and (3) circuits compiled once with calibration data can be reliably reused across multiple calibration cycles and time periods without significant loss in fidelity. These results suggest that the classical overhead associated with daily, per-circuit noise-aware transpilation may not be justified. We propose lightweight alternatives that reduce this overhead without sacrificing fidelity -- offering a path to more efficient and scalable quantum workflows.

Problem

• Evaluating necessity of frequent noise-adaptive quantum transpilation
• Assessing workload concentration and error variability in transpilation
• Exploring reusable transpiled circuits across calibration cycles

Innovation

• Noise-aware transpilation optimizes quantum circuit fidelity
• Random mapping reduces error variability effectively
• Single calibration reuse maintains fidelity over time