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

• URL: http://arxiv.org/abs/2602.22851v1
• Date: Thu, 26 Feb 2026 10:38:53 GMT
• Title: Experimental demonstration of the absence of noise-induced barren plateaus using information content landscape analysis
• Authors: Sebastian Schmitt, Linus Ekstrøm, Alberto Bottarelli, Xavier Bonet-Monroig,
• Abstract summary: Variational quantum algorithms are a promising tool for near-term quantum computing.<n>However, their performance is limited by Barren Plateaus (NIBP)<n>NIBP are predicted to arise due to noise accumulation independent of circuit structure.
• Score: 1.3066182802188202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Variational quantum algorithms are a very promising tool for near-term quantum computing. However, despite their flexibility and wide applicability, their performance is fundamentally limited by Barren Plateaus (BP), where gradients vanish and optimization becomes intractable. Noise-Induced Barren Plateaus (NIBP) are particularly interesting, as they are predicted to arise due to noise accumulation independent of circuit structure. We experimentally study NIBP on IBM quantum hardware and demonstrate their absence under non-unital amplitude damping characterized by the qubit's $T_1$ coherence times. We use Information Content Landscape Analysis (ICLA) to efficiently estimate gradient norms for circuits ranging from 8 to 102 qubits, with hundreds of parameters and circuit runtimes of hundreds of microseconds. Classical simulations of the 8-qubit case under noiseless, depolarizing, amplitude damping, and dephasing noise models serve as a baseline comparison. We thoroughly analyze the experimental results considering calibration data, shot-noise, and circuit structure. We robustly observe that the gradient magnitude saturates beyond a characteristic circuit runtime, in contrast with the exponential decay expected from NIBP. Using recent theoretical results, we corroborate that under $T_1$-dominated noise NIBP do not occur and extract an effective $T_1^\text{eff}$ that is significantly shorter than suggested by standard calibration data. Our results experimentally confirm recent predictions on the absence of NIBP under non-unital noise. These findings also indicate that conventional benchmarking metrics based on average values for device characteristics may be insufficient to predict variational algorithm performance, but full distributions need to be considered.

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