Soft information decoding with superconducting qubits

TL;DR

This paper demonstrates that utilizing full analog measurement data ('soft information') in decoding significantly improves error thresholds in superconducting qubit error correction, enabling more efficient fault-tolerant quantum computing.

Contribution

It introduces a soft decoding approach that leverages analog measurement data, increasing error thresholds by 25% and showing practical benefits for real-time quantum error correction.

Findings

Soft decoding raises the error threshold by 25%.

Using one byte of data per measurement achieves optimal decoding.

Soft decoding reduces error rates up to 30 times.

Abstract

Quantum error correction promises a viable path to fault-tolerant computations, enabling exponential error suppression when the device's error rates remain below the protocol's threshold. This threshold, however, strongly depends on the classical method used to decode the syndrome measurements. These classical algorithms traditionally only interpret binary data, ignoring valuable information contained in the complete analog measurement data. In this work, we leverage this richer "soft information" to decode repetition code experiments implemented on superconducting hardware. We find that "soft decoding" can raise the threshold by 25%, yielding up to 30 times lower error rates. Analyzing the trade-off between information volume and decoding performance we show that a single byte of information per measurement suffices to reach optimal decoding. This underscores the effectiveness and practicality of soft decoding on hardware, including in time-sensitive contexts such as real-time decoding.