Experimental demonstration of the absence of noise-induced barren plateaus using information content landscape analysis

TL;DR

This study experimentally shows that noise-induced barren plateaus do not occur as previously predicted, using information content landscape analysis on IBM quantum hardware, with results supported by classical simulations.

Contribution

We demonstrate experimentally that NIBP can be inhibited by certain noise types, challenging prior theoretical predictions and highlighting the importance of calibration metrics.

Findings

Gradient magnitudes saturate beyond a certain runtime.

$T_1$-dominated noise inhibits NIBP.

Calibration metrics may not predict performance accurately.

Abstract

Variational quantum algorithms are promising candidates for near-term quantum computing but can be hindered by barren plateaus, where gradients vanish exponentially and optimization becomes intractable. Noise-Induced Barren Plateaus (NIBP) are particularly concerning because they are predicted to arise generically from noise accumulation, independent of system size, circuit structure, and observable locality. We experimentally investigate NIBP on IBM quantum hardware. Using Information Content Landscape Analysis (ICLA), we efficiently estimate gradient norms for variational circuits ranging from 8 to 102 qubits, up to hundreds of parameters and circuit runtimes of hundreds of microseconds. Contrary to NIBP expectations, we observe that gradient magnitudes saturate beyond a characteristic runtime rather than decaying exponentially. Classical simulations of the 8-qubit case under noiseless, depolarizing, amplitude-damping, and dephasing noise models support this behavior. Consistent with recent theory, our results show that $T_1$-dominated non-unital noise inhibits the emergence of NIBP. Our analysis suggest that average calibration metrics may be insufficient to predict variational algorithm performance.