Neural-Network Decoders for Quantum Error Correction using Surface Codes:A Space Exploration of the Hardware Cost-Performance Trade-Offs

Abstract

Quantum Error Correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their hardware implementation has not been presented yet. This work presents a space exploration of fully-connected feed-forward NN decoders for small distance surface codes. The goal is to optimize the neural network for high decoding performance, while keeping a minimalistic hardware implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We demonstrate that hardware based NN-decoders can achieve high decoding performance comparable to other state-of-the-art decoding algorithms whilst being well below the tight delay requirements $(\approx 440\mathrm{ns})$ of current solid-state qubit technologies for both ASIC designs $(<30\mathrm{ns})$ and FPGA implementations $(<90\mathrm{ns})$. These results designates NN-decoders as fitting candidates for an integrated hardware implementation in future large-scale quantum computers.

19 pages, 21 figures, 5 papers

References in corpus (8)

• Charge insensitive qubit design derived from the Cooper pair box
• Surface codes: Towards practical large-scale quantum computation
• An addressable quantum dot qubit with fault-tolerant control fidelity
• Suppressing Charge Noise Decoherence in Superconducting Charge Qubits
• Efficient Algorithms for Maximum Likelihood Decoding in the Surface Code
• Optimal and Efficient Decoding of Concatenated Quantum Block Codes
• Spiderweb array: A sparse spin-qubit array
• Cryogenic characterization of 28nm FD-SOI ring oscillators with energy efficiency optimization

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• Neural network decoder for near-term surface-code experiments
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• Artificial Neural Network Syndrome Decoding on IBM Quantum Processors
• Improved Belief Propagation Decoding Algorithms for Surface Codes
• Local Clustering Decoder as a fast and adaptive hardware decoder for the surface code
• Neural network accelerator for quantum control
• Low-overhead quantum error correction codes with a cyclic topology
• Synchronization for Fault-Tolerant Quantum Computers
• Neural Decoders for Universal Quantum Algorithms
• Managing Classical Processing Requirements for Quantum Error Correction