Year: 2025
Type: preprint
Abstract: Quantum resources enable one to achieve quantum-enhanced estimation sensitivity beyond its classical counterpart. Many studies mainly focus on reducing statistical error, under the assumption that one can always set an unbiased estimator. However, setting an unbiased estimator is not always feasible, especially when one cannot fully characterize noise. Such incomplete noise characterization induces a bias and eventually makes it impossible to attain the enhanced-estimation. In this work, we explore two systematic approaches; quantum error correction (QEC) and the virtual purification (VP) to reduce the bias, and compare their performance. First, we show that when the noise is indistinguishable from the signal, QEC cannot reduce the bias since it is impossible to construct a QEC code that corrects the noise while preserving the signal. We then show that VP can mitigate indistinguishable error that eventually enable a more accurate estimation compared to QEC. Our findings reveal that VP offers a robust alternative to QEC in scenarios where indistinguishable errors pose significant challenges. We then demonstrate that VP with a stabilizer state probe can efficiently suppress the bias under local depolarizing noise, thereby yielding a significant improvement in estimation performance compared to the QEC-based approach.
Source: arXiv (Cornell University)
Authors: Kwon, Hyukgun, Oh, Changhun, Lim, Youngrong, Jeong, Hyunseok, Lee, Seung-Woo, Jiang, Liang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Defense Advanced Research Projects Agency, Multidisciplinary University Research Initiative, Institute for Information and Communications Technology Promotion, Air Force Office of Scientific Research, Army Research Office
Awards: G1034473731, G1273934705, G1404049065, G1432373144, QuSeC-TAQS: Nanoscale Covariance Magnetometry with Diamond Quantum Sensors, G1523888516, G1762992887, G1932254719, G2193698, G2882288540, G3162985271, G326219201, G3722242190, G3932724887, G4149761754, G431725244, G4364910921, G4480704047, G4496842895, G4516736450, G45824782, QuIC - TaQS: Interconnects for a Superconducting-Atomic Hybrid Quantum Network, G4700831490, G4848018484, G4876042962, G5231341540, G5659690140, G5893730296, G6167497367, G6200072800, G6390571125, G6518383159, G6526646931, G6547089213, G6754093111, G6762893308, G7033135252, G7420521838, G7452299184, G754601064, NSF Engineering Research Center for Quantum Networks (CQN), G7796153478, G793425364, Collaborative Research: FET: Medium: Design and Implementation of Quantum Databases, QII-TAQS: Quantum Metrological Platform for Single-Molecule Bio-Sensing
OpenAlex ID: W6979311809
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