Reinforcement Learning assisted Pulse Shaping for Superconducting Qubit Readout
ORAL
Abstract
The readout performance of resource-efficient quantum processors comprising multiple superconducting qubits is often not on par with that of qubit-gate operations. Nonidealities such as crosstalk limit the readout performance. Some of these nonidealities can be mitigated or compensated by qubit-state discrimination or qubit-readout pulse shaping. Quantum error correction protocols depend on fast and efficient readout. Quick resonator ring-up and ring-down in a dispersive readout scheme ensure fast measurements and limited qubit dephasing in future operations. This talk focuses on readout pulse shaping for multiple superconducting qubits using deep reinforcement learning. Relative to conventional readout methods, our results reveal that deep reinforcement learning can significantly reduce measurement times.
*This research was funded in part by the DARPA Polyplexus grant No. HR00112010001; by the U.S. Army Research Office (ARO) Multidisciplinary University Research Initiative (MURI) W911NF-18-1-0218; and by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the US Government.
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Presenters
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Benjamin Lienhard
- Massachusetts Institute of Technology MIT