Noise-agnostic quantum error mitigation with data augmented neural models
Abstract Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural netwo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-025-00960-y |
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| _version_ | 1832594522096271360 |
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| author | Manwen Liao Yan Zhu Giulio Chiribella Yuxiang Yang |
| author_facet | Manwen Liao Yan Zhu Giulio Chiribella Yuxiang Yang |
| author_sort | Manwen Liao |
| collection | DOAJ |
| description | Abstract Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware. |
| format | Article |
| id | doaj-art-f70527d2a067471ea758083529b5a236 |
| institution | Kabale University |
| issn | 2056-6387 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj-art-f70527d2a067471ea758083529b5a2362025-01-19T12:34:12ZengNature Portfolionpj Quantum Information2056-63872025-01-0111111210.1038/s41534-025-00960-yNoise-agnostic quantum error mitigation with data augmented neural modelsManwen Liao0Yan Zhu1Giulio Chiribella2Yuxiang Yang3QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong KongQICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong KongQICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong KongQICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong KongAbstract Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.https://doi.org/10.1038/s41534-025-00960-y |
| spellingShingle | Manwen Liao Yan Zhu Giulio Chiribella Yuxiang Yang Noise-agnostic quantum error mitigation with data augmented neural models npj Quantum Information |
| title | Noise-agnostic quantum error mitigation with data augmented neural models |
| title_full | Noise-agnostic quantum error mitigation with data augmented neural models |
| title_fullStr | Noise-agnostic quantum error mitigation with data augmented neural models |
| title_full_unstemmed | Noise-agnostic quantum error mitigation with data augmented neural models |
| title_short | Noise-agnostic quantum error mitigation with data augmented neural models |
| title_sort | noise agnostic quantum error mitigation with data augmented neural models |
| url | https://doi.org/10.1038/s41534-025-00960-y |
| work_keys_str_mv | AT manwenliao noiseagnosticquantumerrormitigationwithdataaugmentedneuralmodels AT yanzhu noiseagnosticquantumerrormitigationwithdataaugmentedneuralmodels AT giuliochiribella noiseagnosticquantumerrormitigationwithdataaugmentedneuralmodels AT yuxiangyang noiseagnosticquantumerrormitigationwithdataaugmentedneuralmodels |