Robust ultra-shallow shadows
Renato M. S. Farias, Raghavendra D. Peddinti, Ingo Roth, Leandro, Aolita

TL;DR
This paper introduces a noise-robust shadow estimation method for shallow quantum circuits that improves measurement accuracy and efficiency in noisy quantum computing environments, demonstrated through simulations and IBM device experiments.
Contribution
The authors develop a practical protocol for noise mitigation in shadow estimation applicable to low-depth circuits, using tensor-network tools for efficient data analysis.
Findings
Robust ultra-shallow shadows outperform unmitigated shadows as circuit depth increases.
The method achieves about tenfold improvement in fidelity estimation precision.
Optimal circuit depth depends on the noise level, balancing measurement bias and variance.
Abstract
We present a robust shadow estimation protocol for wide classes of low-depth measurement circuits that mitigates noise as long as the effective measurement map including noise is locally unitarily invariant. This is in practice an excellent approximation, encompassing for instance the case of ideal single-qubit Clifford gates composing the first circuit layer of an otherwise arbitrary circuit architecture and even non-Markovian, gate-dependent noise in the rest of the circuit. We argue that for weakly-correlated local noise, the measurement channel has an efficient matrix-product representation, and show how to estimate this directly from experimental data using tensor-network tools, eliminating the need for analytical or numeric calculations. We illustrate the relevance of our method with both numerics and proof-of-principle experiments on an IBM Quantum device. Numerically, we show…
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Taxonomy
TopicsFlood Risk Assessment and Management · Landslides and related hazards · Remote Sensing and LiDAR Applications
