Joonas Majaniemi, Elisha S. Matekole (Apr 07 2025).
Abstract: Imperfect measurements are a prevalent source of error across quantum computing platforms, significantly degrading the logical error rates achievable on current hardware. To mitigate this issue, rich measurement data referred to as soft information has been proposed to efficiently identify and correct measurement errors as they occur. In this work, we model soft information decoding across a variety of physical qubit platforms and decoders and showcase how soft information can make error correction viable at lower code distances and higher physical error rates than is otherwise possible. We simulate the effects of soft information decoding on quantum memories for surface codes and bivariate bicycle codes, and evaluate the error suppression performance of soft decoders against traditional decoders. For near-term devices with noise regimes close to the surface code threshold, our simulations show that soft information decoding can provide up to 10% higher error suppression on superconducting qubits and up to 20% stronger error suppression on neutral atom qubits.
