AI-Enabled Decoding of Qubit Loss for Quantum Error-Correcting Codes

TL;DR

This paper introduces an AI-based decoder using a spatiotemporal Graph Neural Network to improve qubit loss correction in quantum error correction, enhancing accuracy and speed.

Contribution

It develops a novel STGNN architecture for simultaneous correction of Pauli errors and identification of qubit loss locations, outperforming traditional algorithms.

Findings

Achieves higher logical accuracy than MWPM and delayed-erasure MWPM decoders.

Identifies over 90% of loss locations within ten measurement rounds.

Performs nearly as well as AlphaQubit but with faster inference due to parallel input structure.

Abstract

Qubit loss is a major source of error in quantum computation, as it invalidates the algebraic structure of the standard stabilizer formalism for quantum error-correcting codes. On the one hand, it complicates decoding; on the other hand, it introduces stochastic flicker patterns in stabilizers as a hallmark of qubit loss. Here, we develop an artificial-intelligence-enabled decoder based on a spatiotemporal Graph Neural Network (STGNN) architecture to extract spatial and temporal correlations from syndrome histories. Our decoder performs a dual-head task, simultaneously correcting standard Pauli errors and identifying the locations of qubit loss. Our decoder achieves significantly higher logical accuracy than both the traditional minimum-weight perfect matching (MWPM) algorithm and even delayed-erasure MWPM decoders that use qubit loss information from the final round as input. Our decoder can also identify more than 90% of loss locations after accumulating stabilizer measurements over the subsequent ten rounds, thereby facilitating qubit reinitialization, for instance, via the continuous loading technique on the atom array platform. For both tasks, our STGNN performs nearly identically to a modified version of AlphaQubit, but it employs a parallel input structure, giving it an advantage in inference time over modified AlphaQubit's recurrent input structure. This work provides a robust and scalable framework for correcting qubit loss errors, paving the way for more efficient fault-tolerant quantum computation.