Efficient Concatenated Bosonic Code for Additive Gaussian Noise
- URL: http://arxiv.org/abs/2102.01374v3
- Date: Mon, 27 Nov 2023 05:41:03 GMT
- Title: Efficient Concatenated Bosonic Code for Additive Gaussian Noise
- Authors: Kosuke Fukui and Takaya Matsuura and Nicolas C. Menicucci
- Abstract summary: Bosonic codes offer noise resilience for quantum information processing.
We propose using a Gottesman-Kitaev-Preskill code to detect discard error-prone qubits and a quantum parity code to handle residual errors.
Our work may have applications in a wide range of quantum computation and communication scenarios.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bosonic codes offer noise resilience for quantum information processing. Good
performance often comes at a price of complex decoding schemes, limiting their
practicality. Here, we propose using a Gottesman-Kitaev-Preskill (GKP) code to
detect and discard error-prone qubits, concatenated with a quantum parity code
to handle the residual errors. Our method employs a simple, linear-time decoder
that nevertheless offers significant performance improvements over the standard
decoder. Our work may have applications in a wide range of quantum computation
and communication scenarios.
Related papers
- 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) - Learning Linear Block Error Correction Codes [62.25533750469467]
We propose for the first time a unified encoder-decoder training of binary linear block codes.
We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
arXiv Detail & Related papers (2024-05-07T06:47:12Z) - Near-optimal decoding algorithm for color codes using Population Annealing [44.99833362998488]
We implement a decoder that finds the recovery operation with the highest success probability.
We study the decoder performance on a 4.8.8 color code lattice under different noise models.
arXiv Detail & Related papers (2024-05-06T18:17:42Z) - Small Quantum Codes from Algebraic Extensions of Generalized Bicycle
Codes [4.299840769087443]
Quantum LDPC codes range from the surface code, which has a vanishing encoding rate, to very promising codes with constant encoding rate and linear distance.
We devise small quantum codes that are inspired by a subset of quantum LDPC codes, known as generalized bicycle (GB) codes.
arXiv Detail & Related papers (2024-01-15T10:38:13Z) - 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) - Neural Belief Propagation Decoding of Quantum LDPC Codes Using
Overcomplete Check Matrices [60.02503434201552]
We propose to decode QLDPC codes based on a check matrix with redundant rows, generated from linear combinations of the rows in the original check matrix.
This approach yields a significant improvement in decoding performance with the additional advantage of very low decoding latency.
arXiv Detail & Related papers (2022-12-20T13:41:27Z) - Quantum Error Correction via Noise Guessing Decoding [0.0]
Quantum error correction codes (QECCs) play a central role in both quantum communications and quantum computation.
This paper shows that it is possible to both construct and decode QECCs that can attain the maximum performance of the finite blocklength regime.
arXiv Detail & Related papers (2022-08-04T16:18:20Z) - 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) - Efficiently computing logical noise in quantum error correcting codes [0.0]
We show that measurement errors on readout qubits manifest as a renormalization on the effective logical noise.
We derive general methods for reducing the computational complexity of the exact effective logical noise by many orders of magnitude.
arXiv Detail & Related papers (2020-03-23T19:40:56Z)
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.