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Lecture notes in computer science

Reducing the Number of Qubits in Quantum Information Set Decoding

Clémence Chevignard, Pierre-Alain Fouque, André Schrottenloher

Published Dec 11, 2024• 7 citations
Publisher
DOI
10.1007/978-981-96-0944-4_10

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The reliability-based decoding approach using the reprocessing of the most reliable information set only is extended into the iterative reprocessing of several information sets. At the end of each information set reprocessing, some information bits are delivered by the decoder. Consequently, information sets with decreasing cardinality values are considered at each iteration. A tight upper bound on the error performance achieved by this new method is derived. Compared to previously proposed competitive approaches, this new method reduces the number of candidate codewords needed to achieve practically optimum decoding. Importantly, it also preserves the very simple structured implementation of the order statistic decoding.
Quantum memory systems are vital in quantum information processing for dependable storage and retrieval of quantum states. Inspired by classical reliability theories that synthesize reliable computing systems from unreliable components, we formalize the problem of reliable storage of quantum information using noisy components. We introduce the notion of stable quantum memories and define the storage rate as the ratio of the number of logical qubits to the total number of physical qubits as well as the circuit complexity of the decoder, which includes both quantum gates and measurements. We demonstrate that a strictly positive storage rate can be achieved by constructing a quantum memory system with quantum expander codes. Moreover, by reducing the reliable storage problem to reliable quantum communication, we provide upper bounds on the achievable storage capacity. In the case of physical qubits corrupted by noise satisfying hypercontractivity conditions, we provide a tighter upper bound on storage capacity using an entropy dissipation argument. Furthermore, observing that the time complexity of the decoder scales nontrivially with the number of physical qubits, achieving asymptotic rates may not be possible due to the induced dependence of the noise on the number of physical qubits. In this constrained nonasymptotic setting, we derive upper bounds on storage capacity using finite blocklength communication bounds. Finally, we numerically analyze the gap between upper and lower bounds in both asymptotic and nonasymptotic cases, and provide suggestions to tighten the gap.
In this paper we propose a new soft decision decoding algorithm for binary block codes, based on information sets, aiming at a low complexity and suitable for hardware and FPGA implementation. In this new algorithm, a preprocessed list of information sets is utilized to generate the candidate code words. The acceptance criterion of Barros, Godoy and Wille (BGW) is used to reduce the number of candidate words evaluated during decoding process. The performance of the proposed algorithm is nearly identical to the maximum likelihood decoder (MLD), however it compares favorably in terms of complexity. The performance gap, when compared with the MLD, can be reduced in function of the processing effort, as shown by results obtained from computer simulation for the C(15,7), C(23,12), and C(24,12) binary block codes.