Alexis Morvan

@amorvan.bsky.social

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Quantum Research Scientist at Google Quantum AI

Overview of RL control. (a) Hierarchy of the feedback loops in control of an error-corrected quantum system. The low-level loop with analog control and readout signals (purple) occurs on a time scale of one QEC cycle; the logical algorithm’s digital feedback loop (green) occurs on a time scale of the decoding latency; the learning feedback loop (pink), presented in this work, is not synchronized with the lower levels, and occurs on a time scale determined by the relevant system drift. The indicated time scales are characteristic of superconducting circuits quantum computing platform.(b) A small space-time chunk of the QEC circuit for the repetition code, highlighting two overlapping detecting regions.(c) One iteration of the learning process. In each epoch, a batch of control policy candidates is sampled from the policy distribution. A certain number of QEC cycles is executed with each policy candidate (shades of red and blue). The acquired QEC data is used to compute rewards by estimating error detection rates for each detector. This information, indicating the relative performance of each policy candidate, is converted by the learning algorithm into a small gradient step of the policy distribution. Then, a new batch of policy candidates is sampled and the process repeats.
The two end of cycles of the surface code with 3-couplers
Lattice surface of a surface code with a Color code.
End cycle stabilizer representation for four surface code implementation: Standard, the standard implementation used so far by every experiments. Hexagonal, where the surface code uses only gates on a hexagonal lattice. The end cycles of even and odd are different. The Walking implementation where data and ancilla exchanges roles and the iSWAP implementation that uses an iSWAP instead of a CZ/CNOT, giving the stabilizer the shape of arrowhead.