The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a novel transit radio telescope operating across the 400–800 MHz band. CHIME is composed of four 20 m × 100 m semicylindrical paraboloid reflectors, each of which has 256 dual-polarization feeds suspended along its axis, giving it a ≳200 deg2 field of view. This, combined with wide bandwidth, high sensitivity, and a powerful correlator, makes CHIME an excellent instrument for the detection of fast radio bursts (FRBs). The CHIME Fast Radio Burst Project (CHIME/FRB) will search beam-formed, high time and frequency resolution data in real time for FRBs in the CHIME field of view. Here we describe the CHIME/FRB back end, including the real-time FRB search and detection software pipeline, as well as the planned offline analyses. We estimate a CHIME/FRB detection rate of 2–42 FRBs sky–1 day–1 normalizing to the rate estimated at 1.4 GHz by Vander Wiel et al. Likely science outcomes of CHIME/FRB are also discussed. CHIME/FRB is currently operational in a commissioning phase, with science operations expected to commence in the latter half of 2018.

No abstract

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identity bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.