Realizing repeated quantum error correction in a distance-three surface code

Krinner, Sebastian; Lacroix, Nathan; Remm, Ants; Paolo, Agustín Di; Genois, Élie; Leroux, Catherine; Hellings, Christoph; Lazar, Stefania; Swiadek, Francois; Herrmann, Johannes M.; Norris, Graham J.; Andersen, Christian Kraglund; Müller, Markus; Blais, Alexandre; Eichler, Christopher; Wallraff, Andreas

doi:10.1038/s41586-022-04566-8

Cited by 272 publications

(144 citation statements)

References 53 publications

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“…Superconducting circuits. First, we consider SC chips, which are developed by various companies and academic groups worldwide [71][72][73][74][75][76][77]. This powerful quantum platform has shown tremendous growth due to the mixture of good scalability and microwave-frequency operation.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

Unsupervised quantum machine learning for fraud detection

Kyriienko¹,

Magnusson²

2022

Preprint

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We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and unsupervised machine learning methods, where average precision is chosen as a robust metric for detecting anomalous data. We focus on kernel-based approaches for ease of direct comparison, basing our unsupervised modelling on one-class support vector machines (OC-SVM). Next, we employ quantum kernels of different type for performing anomaly detection, and observe that quantum FD can challenge equivalent classical protocols at increasing number of features (equal to the number of qubits for data embedding). Performing simulations with registers up to 20 qubits, we find that quantum kernels with re-uploading demonstrate better average precision, with the advantage increasing with system size. Specifically, at 20 qubits we reach the quantum-classical separation of average precision being equal to 15%. We discuss the prospects of fraud detection with near-and mid-term quantum hardware, and describe possible future improvements.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Unsupervised quantum machine learning for fraud detection

Kyriienko¹,

Magnusson²

2022

Preprint

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“…Each CNOT is assumed to take 100ns, and the measurement and reset time of the ancillas take 1µs, as is the case for instance in Ref. [62]. Therefore we set T s = 1.4µs.…”

Section: Basis Expressed Asmentioning

confidence: 99%

Techniques for combining fast local decoders with global decoders under circuit-level noise

Chamberland¹,

Gonçalves²,

Sivarajah³

et al. 2022

Preprint

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Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requirements. We then introduce the construction of local neural network (NN) decoders using three-dimensional convolutions. These local decoders are adapted to circuit-level noise and can be applied to surface code volumes of arbitrary size. Their application removes errors arising from a certain number of faults, which serves to substantially reduce the syndrome density. Remaining errors can then be corrected by a global decoder, such as Blossom or Union Find, with their implementation significantly accelerated due to the reduced syndrome density. However, in the circuit-level setting, the corrections applied by the local decoder introduce many vertical pairs of highlighted vertices. To obtain a low syndrome density in the presence of vertical pairs, we consider a strategy of performing a syndrome collapse which removes many vertical pairs and reduces the size of the decoding graph used by the global decoder. We also consider a strategy of performing a vertical cleanup, which consists of removing all local vertical pairs prior to implementing the global decoder. Lastly, we estimate the cost of implementing our local decoders on Field Programmable Gate Arrays (FPGAs).

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“…It is with this motivation that many experiments have been performed as proofs-of-principle for the components of quantum error correction circuits as well as demonstrating error suppression in certain restricted cases (see [2][3][4][5][6][7][8] for selected recent examples, and references therein). The most recent examples have made the major breakthrough of demonstrating surface codes of distance 3 [9,10].…”

Section: Introductionmentioning

confidence: 99%

Syndrome-Derived Error Rates as a Benchmark of Quantum Hardware

Wootton¹

2022

Preprint

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Realizing repeated quantum error correction in a distance-three surface code

Cited by 272 publications

References 53 publications

Unsupervised quantum machine learning for fraud detection

Unsupervised quantum machine learning for fraud detection

Techniques for combining fast local decoders with global decoders under circuit-level noise

Syndrome-Derived Error Rates as a Benchmark of Quantum Hardware

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