Optimal post-processing for a generic single-shot qubit readout

We analyze three different post-processing methods applied to a single-shot qubit readout: the average-signal (boxcar filter), peak-signal, and maximum-likelihood methods. In contrast to previous work, we account for a stochastic turn-on time \(t_i\) associated with the leading edge of a pulse signaling one of the qubit states. This model is relevant to spin-qubit readouts based on spin-to-charge conversion and would be generically reached in the limit of large signal-to-noise ratio \(r\) for several other physical systems, including fluorescence-based readouts of ion-trap qubits and nitrogen-vacancy center spins. We derive analytical closed-form expressions for the conditional probability distributions associated with the peak-signal and boxcar filters. For the boxcar filter, we find an asymptotic scaling of the single-shot error rate \(\varepsilon \sim \ln r/\sqrt{r}\) when \(t_i\) is stochastic, in contrast to the result \(\varepsilon \sim \ln r/ r\) for deterministic \(t_i\). Consequently, the peak-signal method outperforms the boxcar filter significantly when \(t_i\) is stochastic, but is only marginally better for deterministic \(t_i\) (a result that is consistent with the widespread use of the boxcar filter for fluorescence-based readouts and the peak-signal for spin-to-charge conversion). We generalize the theoretically optimal maximum-likelihood method to stochastic \(t_i\) and show numerically that a stochastic turn-on time \(t_i\) will always result in a larger single-shot error rate. Based on this observation, we propose a general strategy to improve the quality of single-shot readouts by forcing \(t_i\) to be deterministic.