Hybrid Stabilizer Matrix Product Operator

We introduce a novel hybrid approach combining tensor network methods with the stabilizer formalism to address the challenges of simulating many-body quantum systems. By integrating these techniques, we enhance our ability to accurately model unitary dynamics while mitigating the exponential growth of entanglement encountered in classical simulations. We demonstrate the effectiveness of our method through applications to random Clifford T-doped circuits and random Clifford Floquet dynamics. This approach offers promising prospects for advancing our understanding of complex quantum phenomena and accelerating progress in quantum simulation.

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Publication Details

Journal
Physical Review Letters
Published
2024-10-11
DOI
https://doi.org/10.1103/physrevlett.133.150604
Citations
21
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
5.90

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article

Hybrid Stabilizer Matrix Product Operator

Alessandro Santini, Mario Collura, Antonio Francesco Mello
21 citations
Physical Review Letters
Model Reduction and Neural Networks
5.90
article

Hybrid Stabilizer Matrix Product Operator

Alessandro Santini, Mario Collura, Antonio Francesco Mello
article en
21 citations

Abstract

We introduce a novel hybrid approach combining tensor network methods with the stabilizer formalism to address the challenges of simulating many-body quantum systems. By integrating these techniques, we enhance our ability to accurately model unitary dynamics while mitigating the exponential growth of entanglement encountered in classical simulations. We demonstrate the effectiveness of our method through applications to random Clifford T-doped circuits and random Clifford Floquet dynamics. This approach offers promising prospects for advancing our understanding of complex quantum phenomena and accelerating progress in quantum simulation.

Physical Review LettersVol. 133(15)
Scuola Internazionale Superiore di Studi Avanzati (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Trieste (IT)
Ministero dell'Università e della Ricerca
Openalex Percentile: Top 3%
Model Reduction and Neural Networks
5.90
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