Noise-Robust Estimation of Quantum Observables in Noisy Hardware

Fuente: arXiv
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Autores principales: Hosseinkhani, Amin, Šimkovic, Fedor, Calzona, Alessio, Godinez-Ramirez, Emiliano, Pina-Canelles, Vicente, Liu, Tianhan, Guimarães, José D., Auer, Adrian, de Vega, Inés
Formato: Preprint
Publicado: 2025
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author Hosseinkhani, Amin
Šimkovic, Fedor
Calzona, Alessio
Godinez-Ramirez, Emiliano
Pina-Canelles, Vicente
Liu, Tianhan
Guimarães, José D.
Auer, Adrian
de Vega, Inés
author_facet Hosseinkhani, Amin
Šimkovic, Fedor
Calzona, Alessio
Godinez-Ramirez, Emiliano
Pina-Canelles, Vicente
Liu, Tianhan
Guimarães, José D.
Auer, Adrian
de Vega, Inés
contents Error mitigation is essential for extracting reliable results from quantum computations performed on noisy intermediate-scale quantum hardware. Here we introduce Noise-Robust Estimation (NRE), a noise-agnostic framework that suppresses estimation bias through a two-stage post-processing protocol. The method combines measurement data from a target circuit and a corresponding noise-canceling companion circuit to construct a baseline estimator with reduced sensitivity to noise. We show that the residual bias of this estimator is governed by the variation of an auxiliary quantity across amplified noise realizations, motivating the use of a measurable diagnostic quantity: the normalized dispersion of this auxiliary estimator. When the dispersion approaches zero, contributions arising from imperfect noise amplification vanish and the remaining bias terms are expected to diminish for smooth stationary noise profiles. Leveraging this relationship, NRE performs a final extrapolation to the zero-dispersion limit using bootstrapped measurement data. We experimentally validate the method on a 20-qubit IQM superconducting quantum processor using circuits containing up to 480 entangling CZ gates. Across a variety of circuits and noise levels, NRE consistently achieves substantially reduced bias compared to existing mitigation techniques while maintaining moderate sampling overhead. These results establish NRE as a practical and broadly applicable error-mitigation strategy for quantum computations on noisy hardware.
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publishDate 2025
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spellingShingle Noise-Robust Estimation of Quantum Observables in Noisy Hardware
Hosseinkhani, Amin
Šimkovic, Fedor
Calzona, Alessio
Godinez-Ramirez, Emiliano
Pina-Canelles, Vicente
Liu, Tianhan
Guimarães, José D.
Auer, Adrian
de Vega, Inés
Quantum Physics
Error mitigation is essential for extracting reliable results from quantum computations performed on noisy intermediate-scale quantum hardware. Here we introduce Noise-Robust Estimation (NRE), a noise-agnostic framework that suppresses estimation bias through a two-stage post-processing protocol. The method combines measurement data from a target circuit and a corresponding noise-canceling companion circuit to construct a baseline estimator with reduced sensitivity to noise. We show that the residual bias of this estimator is governed by the variation of an auxiliary quantity across amplified noise realizations, motivating the use of a measurable diagnostic quantity: the normalized dispersion of this auxiliary estimator. When the dispersion approaches zero, contributions arising from imperfect noise amplification vanish and the remaining bias terms are expected to diminish for smooth stationary noise profiles. Leveraging this relationship, NRE performs a final extrapolation to the zero-dispersion limit using bootstrapped measurement data. We experimentally validate the method on a 20-qubit IQM superconducting quantum processor using circuits containing up to 480 entangling CZ gates. Across a variety of circuits and noise levels, NRE consistently achieves substantially reduced bias compared to existing mitigation techniques while maintaining moderate sampling overhead. These results establish NRE as a practical and broadly applicable error-mitigation strategy for quantum computations on noisy hardware.
title Noise-Robust Estimation of Quantum Observables in Noisy Hardware
topic Quantum Physics
url https://arxiv.org/abs/2503.06695