Year: 2024
Type: article
Abstract: Topological quantum codes, such as toric and surface codes, are excellent candidates for hardware implementation due to their robustness against errors and their local interactions between qubits. However, decoding these codes efficiently remains a challenge: existing decoders often fall short of meeting requirements such as having low computational complexity (ideally linear in the code’s blocklength), low decoding latency, and low power consumption. In this paper we propose a novel bit-flipping (BF) decoder tailored for toric and surface codes. We introduce the proximity vector as a heuristic metric for flipping bits, and we develop a new subroutine for correcting degenerate multiple errors on adjacent qubits. Our algorithm has quadratic complexity growth and it can be efficiently implemented as it does not require operations on dynamic memories, as do state-of-art decoding algorithms such as minimum weight perfect matching or union find. The proposed decoder shows a decoding threshold of 7.5% for the 2D toric code and 7% for the rotated planar code over the binary symmetric channel.
Institutions: University of Arizona, University College Dublin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CIF: Medium: Iterative Quantum LDPC Decoders, G2422073433, Collaborative Research: CIF: Medium: QODED: Quantum codes Optimized for the Dynamics between Encoded Computation and Decoding using Classical Coding Techniques, ECCS/CCSS: Neural Network Nonlinear Iterative LDPC Decoders with Guaranteed Error Performance and Fast Convergence, G3559249381, G4429192037, NSF Engineering Research Center for Quantum Networks (CQN), G7591833153, Convergent Quantum REsearchAlliance in Telecommunications (CoQREATE), Collaborative Research: Secure and Efficient Post-quantum Cryptography: from Coding Theory to Hardware Architecture, CIF: Small: Learning To Correct Errors
OpenAlex ID: W4402187310