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

TL;DR

This study empirically evaluates the impact of noise-adaptive transpilation in quantum computing, finding that less frequent transpilation may suffice, and proposing lightweight alternatives to improve efficiency.

Contribution

It provides the first comprehensive empirical analysis of noise-adaptive transpilation's necessity and introduces practical methods to reduce classical overhead.

Findings

Noise-aware transpilation concentrates workloads on few qubits, increasing error variability.

Random mapping mitigates workload concentration while maintaining fidelity.

Reusing circuits across calibration cycles does not significantly reduce fidelity.

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.