Machine Learning based Noise Characterization and Correction on Neutral Atoms NISQ Devices

Author:

Canonici Ettore12,Martina Stefano12,Mengoni Riccardo3,Ottaviani Daniele3,Caruso Filippo124ORCID

Affiliation:

1. Department of Physics and Astronomy University of Florence via Sansone 1 Sesto Fiorentino 50019 Italy

2. European Laboratory of Non‐Linear Spesctroscopy (LENS) via Carrara 1 Sesto Fiorentino 50019 Italy

3. CINECA Via Magnanelli 6/3, Casalecchio di Reno Bologna 40033 Italy

4. Istituto Nazionale di Ottica (INO) Consiglio Nazionale delle Ricerche (CNR) via Carrara 1 Sesto Fiorentino 50019 Italy

Abstract

AbstractNeutral atoms devices represent a promising technology using optical tweezers to geometrically arrange atoms and modulated laser pulses to control their quantum states. They are exploited as noisy intermediate‐scale quantum (NISQ) processors. Indeed, like all real quantum devices, they are affected by noise introducing errors in the computation. Therefore, it is important to understand and characterize the noise sources and possibly to correct them. Here, two machine‐learning based approaches are proposed respectively to estimate the noise parameters and to mitigate their effects using only measurements of the final quantum state. Our analysis is then tested on a real neutral atom platform, comparing our predictions with a priori estimated parameters. It turns out that increasing the number of atoms is less effective than using more measurements on a smaller scale. The agreement is not always good but this may be due to the limited amount of real data that are obtained from a still under development device. Finally, reinforcement learning is employed to design a pulse that mitigates the noise effects. Our machine learning‐based approach is espected to be very useful for the noise benchmarking of NISQ processors and, more in general, of real quantum technologies.

Funder

H2020 Future and Emerging Technologies

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computational Theory and Mathematics,Condensed Matter Physics,Mathematical Physics,Nuclear and High Energy Physics,Electronic, Optical and Magnetic Materials,Statistical and Nonlinear Physics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Benchmarking regularisation methods for quantum process tomography on NISQ devices;The European Physical Journal Special Topics;2024-01-09

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