Optimal recovery for quantum error correction

The calculation of the error threshold of quantum error correcting codes typically proceeds as follows. First, syndromes are measured. Then, a decoder infers the error chain and the corresponding correction is applied. The threshold is then defined as the largest correctable error rate, with the maximum-likelihood decoder corresponding to the ``optimal’’ threshold. However, a broader set of operations could be used to recover quantum information. The true optimal threshold should be optimised over all possible recovery schemes, which can be described by quantum channels. Here, we study such optimal recovery channels and their thresholds $p_\mathrm{th}^\mathrm{opt}$. We introduce an information-theoretic quantity, mutual trace distance, which provides a necessary and sufficient diagnostic for sharply determining $p_\mathrm{th}^\mathrm{opt}$ without explicit optimisation. In contrast, previous works give a lower bound on $p_\mathrm{th}^\mathrm{opt}$ by specifying particular recovery schemes, e.g. Schumacher-Westmoreland (SW) which provides coherent information as a diagnostic to lower bound $p^\mathrm{opt}\mathrm{th}$. We prove that the Petz and SW recovery schemes are optimal, i.e. their threshold is $p\mathrm{th}^\mathrm{opt}$. With their optimality established, we explore the structure of optimal and non-optimal recovery schemes and their phase diagrams.

💡 Research Summary

The paper addresses a fundamental limitation in the conventional approach to quantum error correction (QEC), where the recovery operation is restricted to syndrome measurement followed by a decoder and a corrective Pauli operation. By treating the recovery as an arbitrary quantum channel R, the authors formulate the problem of finding the optimal recovery channel R_opt that minimizes a loss function L, chosen as 1 – F_e (the entanglement infidelity). Directly optimizing R_opt is intractable because it requires semi‑definite programming over an exponentially large space. To bypass this, the authors introduce a novel information‑theoretic quantity called the mutual trace distance
(T’_{R:E}=T