Speaker
Description
Quantum error correction will ultimately empower quantum computers to
leverage their full potential. However, substantial device overhead and
the need for frequent syndrome measurements, which are themselves
error-prone, render the demonstration of logical qubits that
significantly surpass break-even still challenging. Autonomous quantum
error correction represents a promising alternative, where an engineered
environment allows to bypass the syndrome measurements. In this talk, I
show how we use reinforcement learning to search for, and find, bosonic
code spaces that can surpass break-even under experimentally feasible
conditions. Bosonic codes are, for instance, available and utilized in
some of the currently most promising and widespread quantum processors
based on superconducting qubits. Surprisingly, when we increase the
search space by relaxing the constraints on ideal quantum error
correction, we find simple and robust code words that significantly
surpass break-even while minimizing device overhead. This RL code not
only reduces device complexity compared to other proposed encodings, but
also outperforms its competitors in terms of its capability to correct
errors.
