Quantum Noise Sensing by Generating Fake Noise

Year: 2022

Authors: Braccia P., Banchi L., Caruso F.

Autors Affiliation: Univ Firenze, Dipartimento Fis & Astron, I-50019 Sesto Fiorentino, Italy; Ist Nazl Fis Nucl, Sez Firenze, I-50019 Sesto Fiorentino, Italy; LENS, Via N Carrara 1, I-50019 Sesto Fiorentino, Italy; QSTAR, Via N Carrara 1, I-50019 Sesto Fiorentino, Italy; Ist Nazl Ottica CNR INO, Florence, Italy.

Abstract: Noisy intermediate-scale quantum (NISQ) devices are nowadays starting to become available to the final user, hence potentially allowing the quantum speedups predicted by quantum-information theory to be shown. However, before implementing any quantum algorithm, it is crucial to have at least a partial or possibly full knowledge on the type and amount of noise affecting the quantum machine. Here, by generalizing quantum generative adversarial learning from quantum states (QGANs) to quantum operations, superoperators, and channels (here named super QGANs), we propose a very promising framework to characterize noise in a realistic quantum device, even in the case of spatially and temporally correlated noise (memory channels) affecting quantum circuits. The key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one. We find that, when applied to the benchmarking case of Pauli channels, the super-QGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise. Moreover, we also show how to employ it for quantum metrology applications. We believe our super QGANs pave the way for the development of hybrid quantum-classical machine-learning protocols for a better characterization and control of the current and future unavoidably noisy quantum devices.

Journal/Review: PHYSICAL REVIEW APPLIED

Volume: 17 (2)      Pages from: 24002-1  to: 24002-11

More Information: L.B. acknowledges support by the program Rita Levi Montalcini for young researchers, Grant No. PGR15V3JYH, funded by Ministero dell’Istruzione, dell’Universita e della Ricerca (MIUR) . F.C. is finan-cially supported by the Fondazione CR Firenze through the project QUANTUM-AI, the PATHOS EU H2020 FET-OPEN Grant No. 828946, and the Florence University Grant No. Q-CODYCES.
KeyWords: Benchmarking; Quantum noise; Quantum optics; Adversarial learning; Correlated noise; Learn+; Quantum algorithms; Quantum channel; Quantum device; Quantum information theory; Quantum machines; Quantum operations; Quantum state
DOI: 10.1103/PhysRevApplied.17.024002

Citations: 5
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