Generalizing the matching decoder for the Chamon code
- URL: http://arxiv.org/abs/2411.03443v1
- Date: Tue, 05 Nov 2024 19:00:12 GMT
- Title: Generalizing the matching decoder for the Chamon code
- Authors: Zohar Schwartzman-Nowik, Benjamin J. Brown,
- Abstract summary: 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.
- Score: 1.8416014644193066
- License:
- Abstract: Different choices of quantum error-correcting codes can reduce the demands on the physical hardware needed to build a quantum computer. To achieve the full potential of a code, we must develop practical decoding algorithms that can correct errors that have occurred with high likelihood. Matching decoders are very good at correcting local errors while also demonstrating fast run times that can keep pace with physical quantum devices. We implement variations of a matching decoder for a three-dimensional, non-CSS, low-density parity check code known as the Chamon code, which has a non-trivial structure that does not lend itself readily to this type of decoding. The non-trivial structure of the syndrome of this code means that we can supplement the decoder with additional steps to improve the threshold error rate, below which the logical failure rate decreases with increasing code distance. 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.
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) - Breadth-first graph traversal union-find decoder [0.0]
We develop variants of the union-find decoder that simplify its implementation and provide potential decoding speed advantages.
We show how these methods can be adapted to decode non-topological quantum low-density-parity-check codes.
arXiv Detail & Related papers (2024-07-22T18:54:45Z) - Fault-Tolerant Quantum Memory using Low-Depth Random Circuit Codes [0.24578723416255752]
Low-depth random circuit codes possess many desirable properties for quantum error correction.
We design a fault-tolerant distillation protocol for preparing encoded states of one-dimensional random circuit codes.
We show through numerical simulations that our protocol can correct erasure errors up to an error rate of $2%$.
arXiv Detail & Related papers (2023-11-29T19:00:00Z) - Testing the Accuracy of Surface Code Decoders [55.616364225463066]
Large-scale, fault-tolerant quantum computations will be enabled by quantum error-correcting codes (QECC)
This work presents the first systematic technique to test the accuracy and effectiveness of different QECC decoding schemes.
arXiv Detail & Related papers (2023-11-21T10:22:08Z) - The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
We introduce a data efficient neural decoder that exploits the symmetries of the problem.
We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
arXiv Detail & Related papers (2023-04-14T19:46:39Z) - Modular decoding: parallelizable real-time decoding for quantum
computers [55.41644538483948]
Real-time quantum computation will require decoding algorithms capable of extracting logical outcomes from a stream of data generated by noisy quantum hardware.
We propose modular decoding, an approach capable of addressing this challenge with minimal additional communication and without sacrificing decoding accuracy.
We introduce the edge-vertex decomposition, a concrete instance of modular decoding for lattice-surgery style fault-tolerant blocks.
arXiv Detail & Related papers (2023-03-08T19:26:10Z) - 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) - A local pre-decoder to reduce the bandwidth and latency of quantum error
correction [3.222802562733787]
A fault-tolerant quantum computer will be supported by a classical decoding system interfacing with quantum hardware.
We propose a local pre-decoder', which makes greedy corrections to reduce the amount of syndrome data sent to a standard matching decoder.
We find substantial improvements in the runtime of the global decoder and the communication bandwidth by using the pre-decoder.
arXiv Detail & Related papers (2022-08-09T11:01:56Z) - 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) - Adversarial Neural Networks for Error Correcting Codes [76.70040964453638]
We introduce a general framework to boost the performance and applicability of machine learning (ML) models.
We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words.
Our framework is game-theoretic, motivated by generative adversarial networks (GANs)
arXiv Detail & Related papers (2021-12-21T19:14:44Z) - Correcting spanning errors with a fractal code [7.6146285961466]
We propose an efficient decoder for the Fibonacci code'; a two-dimensional classical code that mimics the fractal nature of the cubic code.
We perform numerical experiments that show our decoder is robust to one-dimensional, correlated errors.
arXiv Detail & Related papers (2020-02-26T19:00:06Z)
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.