text: 321,20,1420910060721512448,746440524052082688,Nicolas Delfosse,"Check out our new paper on using soft information to improve quantum error correction with Chris Pattison, Michael Beverland, @themarcusps. That was great to have Chris as an intern in our group last Fall! <LINK> Why should you read this paper? 1/ You want better quantum error correction. 2/ Your measurement outcomes look continuous and not only +1 or -1 like in most quantum error correction papers. 3/ You want to understand how to to optimize the measurement time of your physical qubits. I am especially excited about our study of the optimal measurement time. Have a look at Figure 12.",https://arxiv.org/abs/2107.13589,"The typical model for measurement noise in quantum error correction is to randomly flip the binary measurement outcome. In experiments, measurements yield much richer information - e.g., continuous current values, discrete photon counts - which is then mapped into binary outcomes by discarding some of this information. In this work, we consider methods to incorporate all of this richer information, typically called soft information, into the decoding of quantum error correction codes, and in particular the surface code. We describe how to modify both the Minimum Weight Perfect Matching and Union-Find decoders to leverage soft information, and demonstrate these soft decoders outperform the standard (hard) decoders that can only access the binary measurement outcomes. Moreover, we observe that the soft decoder achieves a threshold 25\% higher than any hard decoder for phenomenological noise with Gaussian soft measurement outcomes. We also introduce a soft measurement error model with amplitude damping, in which measurement time leads to a trade-off between measurement resolution and additional disturbance of the qubits. Under this model we observe that the performance of the surface code is very sensitive to the choice of the measurement time - for a distance-19 surface code, a five-fold increase in measurement time can lead to a thousand-fold increase in logical error rate. Moreover, the measurement time that minimizes the physical error rate is distinct from the one that minimizes the logical performance, pointing to the benefits of jointly optimizing the physical and quantum error correction layers. ",Improved quantum error correction using soft information,3,"['Check out our new paper on using soft information to improve quantum error correction with Chris Pattison, Michael Beverland, @themarcusps.\nThat was great to have Chris as an intern in our group last Fall!\n\n<LINK>', 'Why should you read this paper?\n1/ You want better quantum error correction.\n2/ Your measurement outcomes look continuous and not only +1 or -1 like in most quantum error correction papers.\n3/ You want to understand how to to optimize the measurement time of your physical qubits.', 'I am especially excited about our study of the optimal measurement time. Have a look at Figure 12.']",21,07,592