Techniques for combining fast local decoders with global decoders under
circuit-level noise
- URL: http://arxiv.org/abs/2208.01178v2
- Date: Wed, 28 Sep 2022 00:36:30 GMT
- Title: Techniques for combining fast local decoders with global decoders under
circuit-level noise
- Authors: Christopher Chamberland and Luis Goncalves and Prasahnt Sivarajah and
Eric Peterson and Sebastian Grimberg
- Abstract summary: We 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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).
Related papers
- Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code [0.0]
We introduce the Local Clustering Decoder as a solution that simultaneously achieves the accuracy and speed requirements of a real-time decoding system.
Our decoder is implemented on FPGAs and exploits hardware parallelism to keep pace with the fastest qubit types.
It enables one million error-free quantum operations with 4x fewer physical qubits when compared to standard non-adaptive decoding.
arXiv Detail & Related papers (2024-11-15T16:43:59Z) - Generalizing the matching decoder for the Chamon code [1.8416014644193066]
We implement a matching decoder for a three-dimensional, non-CSS, low-density parity check code known as the Chamon code.
We find that a generalized matching decoder that is augmented by a belief-propagation step prior to matching gives a threshold of 10.5% for depolarising noise.
arXiv Detail & Related papers (2024-11-05T19:00:12Z) - Accelerating Error Correction Code Transformers [56.75773430667148]
We introduce a novel acceleration method for transformer-based decoders.
We achieve a 90% compression ratio and reduce arithmetic operation energy consumption by at least 224 times on modern hardware.
arXiv Detail & Related papers (2024-10-08T11:07:55Z) - Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes [3.001631679133604]
We introduce localized statistics decoding for arbitrary quantum low-density parity-check codes.
Our decoder is more amenable to implementation on specialized hardware, positioning it as a promising candidate for decoding real-time syndromes from experiments.
arXiv Detail & Related papers (2024-06-26T18:00:09Z) - Graph Neural Networks for Enhanced Decoding of Quantum LDPC Codes [6.175503577352742]
We propose a differentiable iterative decoder for quantum low-density parity-check (LDPC) codes.
The proposed algorithm is composed of classical belief propagation (BP) decoding stages and intermediate graph neural network (GNN) layers.
arXiv Detail & Related papers (2023-10-26T19:56:25Z) - A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction [0.0]
We design and analyze a neural decoder based on an in-memory crossbar (IMC) architecture.
We develop hardware-aware re-training methods to mitigate the fidelity loss.
This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant QEC.
arXiv Detail & Related papers (2023-07-18T17:46:33Z) - Deep Quantum Error Correction [73.54643419792453]
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing.
In this work, we efficiently train novel emphend-to-end deep quantum error decoders.
The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
arXiv Detail & Related papers (2023-01-27T08:16:26Z) - NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction.
The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network.
A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
arXiv Detail & Related papers (2022-09-29T04:06:00Z) - Graph Neural Networks for Channel Decoding [71.15576353630667]
We showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes.
The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph.
We benchmark our proposed decoder against state-of-the-art in conventional channel decoding as well as against recent deep learning-based results.
arXiv Detail & Related papers (2022-07-29T15:29:18Z) - Improved decoding of circuit noise and fragile boundaries of tailored
surface codes [61.411482146110984]
We introduce decoders that are both fast and accurate, and can be used with a wide class of quantum error correction codes.
Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC.
We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code.
arXiv Detail & Related papers (2022-03-09T18:48:54Z) - Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks.
We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation.
We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
arXiv Detail & Related papers (2022-02-09T18:48:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.