Dear quanta,

At 11am we'll have a group meeting (6-310) and Annie will talk about quantum walks.

At 1:30pm we'll have Steve Flammia (Sydney) speak in the seminar (6C-442).

title: Efficient learning of Pauli channels

abstract: Pauli channels are ubiquitous in quantum information, both as a dominant noise source in many computing architectures and as a practical model for analyzing error correction and fault tolerance. Here we prove several results on efficiently learning Pauli channels, and more generally the Pauli projection of a quantum channel. We first derive a protocol for learning a Pauli channel on n qubits with high probability to a relative precision ε using O(ε^{−2} n 2^n) measurements, which is efficient in the Hilbert space dimension. The estimate is robust to state preparation and measurement errors which, together with the relative precision, makes it especially appropriate for applications involving characterization of high-accuracy quantum gates. Next we show that the error rates for an s-sparse Pauli channel can be estimated to a relative precision ε using O(ε^{−2} s^2 log s) measurements. Finally, we show that when the Pauli channel is given by a Markov field with at most k-local correlations, we can learn an entire n-qubit Pauli channel to relative precision ε with only O_k(ε^{−2} n^2 log n) measurements, which is efficient in the number of qubits. The algorithms themselves are quite practical, and I will show experimental results for full characterization of the noise in a 16 qubit device. These results enable a host of applications beyond just characterizing noise in a large-scale quantum system: they pave the way to tailoring quantum codes, optimizing decoders, and customizing fault tolerance protocols to suit a particular device. This is joint work with Robin Harper and Joel Wallman.