Scalable Neural Decoder for Topological Surface Codes
With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scal...
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| Vydané v: | Physical review letters Ročník 128; číslo 8; s. 080505 |
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| Hlavní autori: | , , |
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25.02.2022
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| Abstract | With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon. |
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| AbstractList | With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon. With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon.With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger quantum circuits which will require fast and scalable algorithms for quantum error correction. Here, we present a neural network based decoder that, for a family of stabilizer codes subject to depolarizing noise and syndrome measurement errors, is scalable to tens of thousands of qubits (in contrast to other recent machine learning inspired decoders) and exhibits faster decoding times than the state-of-the-art union find decoder for a wide range of error rates (down to 1%). The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code, akin to a convolutional neural network in pattern recognition approaches. We show that such a preprocessing step allows to effectively reduce the error rate by up to 2 orders of magnitude in practical applications and, by detecting correlation effects, shifts the actual error threshold up to fifteen percent higher than the threshold of conventional error correction algorithms such as union find or minimum weight perfect matching, even in the presence of measurement errors. An in situ implementation of such a machine learning-assisted quantum error correction will be a decisive step to push the entanglement frontier beyond the NISQ horizon. |
| ArticleNumber | 080505 |
| Author | Park, Chae-Yeun Trebst, Simon Meinerz, Kai |
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| CitedBy_id | crossref_primary_10_1103_PhysRevResearch_6_013154 crossref_primary_10_1016_j_engappai_2025_110808 crossref_primary_10_1038_s41586_024_08148_8 crossref_primary_10_1088_2058_9565_adc3ba crossref_primary_10_1038_s41467_023_42482_1 crossref_primary_10_1002_qute_202500158 crossref_primary_10_1103_PhysRevX_13_031007 crossref_primary_10_1088_2058_9565_ace64d crossref_primary_10_1145_3505637 crossref_primary_10_1007_s11128_024_04377_y crossref_primary_10_1103_PhysRevApplied_19_034050 crossref_primary_10_1103_PhysRevA_111_012419 crossref_primary_10_1038_s42005_024_01883_4 crossref_primary_10_1007_s11128_025_04826_2 crossref_primary_10_1103_PhysRevResearch_7_023181 crossref_primary_10_1088_2399_1984_aceba6 crossref_primary_10_1103_PRXQuantum_6_010360 crossref_primary_10_1038_s41534_025_01033_w crossref_primary_10_1103_PRXQuantum_4_040344 crossref_primary_10_1145_3733239 crossref_primary_10_1103_PhysRevResearch_7_013029 crossref_primary_10_1007_s11128_023_03898_2 crossref_primary_10_1088_1674_1056_adab63 crossref_primary_10_1103_PhysRevResearch_6_L032004 |
| Cites_doi | 10.1103/PhysRevA.96.062302 10.1103/PhysRevLett.108.180501 10.1103/PhysRevX.2.021004 10.22331/q-2019-09-02-183 10.22331/q-2020-08-24-310 10.1103/PhysRevA.102.042411 10.22331/q-2021-12-02-595 10.1103/PhysRevA.76.042319 10.1088/1367-2630/aaf29e 10.1016/j.physleta.2020.126353 10.1038/s41586-019-1666-5 10.1103/PhysRevLett.122.200501 10.1063/1.1499754 10.1088/2632-2153/abc609 10.22331/q-2018-08-06-79 10.1103/PhysRevX.11.031039 10.1090/psapm/068/2762145 10.26421/QIC14.9-10-1 10.1103/PhysRevLett.104.050504 10.1038/s41567-020-0920-y 10.1103/PhysRevA.102.032411 10.4153/CJM-1965-045-4 10.1103/PhysRevResearch.2.033042 10.1088/2058-9565/aad1f7 10.1103/PhysRevLett.119.030501 10.1103/PhysRevResearch.2.023230 10.1016/S0003-4916(02)00018-0 10.1088/2058-9565/aa955a 10.1103/PhysRevA.86.032324 10.1038/s41598-017-11266-1 |
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| References | G. Duclos-Cianci (PhysRevLett.128.080505Cc16R1) 2010 PhysRevLett.128.080505Cc28R1 PhysRevLett.128.080505Cc29R1 PhysRevLett.128.080505Cc26R1 PhysRevLett.128.080505Cc27R1 PhysRevLett.128.080505Cc9R1 PhysRevLett.128.080505Cc7R1 PhysRevLett.128.080505Cc6R1 N P. Jouppi (PhysRevLett.128.080505Cc36R1) 2017 PhysRevLett.128.080505Cc24R1 PhysRevLett.128.080505Cc4R1 PhysRevLett.128.080505Cc25R1 PhysRevLett.128.080505Cc46R1 PhysRevLett.128.080505Cc3R1 PhysRevLett.128.080505Cc22R1 PhysRevLett.128.080505Cc2R1 PhysRevLett.128.080505Cc23R1 PhysRevLett.128.080505Cc44R1 PhysRevLett.128.080505Cc1R1 PhysRevLett.128.080505Cc20R1 PhysRevLett.128.080505Cc21R1 PhysRevLett.128.080505Cc40R1 PhysRevLett.128.080505Cc41R1 PhysRevLett.128.080505Cc15R1 PhysRevLett.128.080505Cc18R1 PhysRevLett.128.080505Cc17R1 E. L. Lawler (PhysRevLett.128.080505Cc39R1) 1976 PhysRevLett.128.080505Cc12R1 PhysRevLett.128.080505Cc13R1 PhysRevLett.128.080505Cc31R1 PhysRevLett.128.080505Cc32R1 PhysRevLett.128.080505Cc10R1 PhysRevLett.128.080505Cc30R1 |
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| Title | Scalable Neural Decoder for Topological Surface Codes |
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