Speaker
Description
Machine-learning residual decoders improve Steane QEC in a feed-forward-free logical two-qubit randomized benchmarking (RB) experiment on neutral-atom platforms such as QuEra, where mid-circuit conditional gates are unavailable. Two logical qubits are encoded in Steane [[7,1,3]] blocks and initialized via the Goto fault-tolerant procedure. Logical two-qubit Clifford RB is applied transversally, while syndrome-extraction rounds are inserted between RB segments using fresh ancillas. All measurements are performed during the protocol but no run-time branching is allowed: syndrome data are read out at the end and correction is handled offline via Pauli-frame tracking (PFT), yielding a fixed circuit suitable for hardware execution.
We develop an analytic baseline decoder, LUT-PFT, that uses Steane distance-three X- and Z-type syndromes with a weight-one lookup table to update the Pauli frame rather than applying physical recovery gates. Our ML approach does not relearn Steane decoding from scratch; it learns a residual correction that captures structured, correlated decoder failures relative to LUT-PFT. Two residual classifiers are studied: BoostTree (XGBoost) and a two-stage neural network, both combined with the analytic Pauli frame, with a confidence-based fallback to LUT-PFT.
Noisy simulations of logical 2Q RB (sequence lengths 60–360; QEC rounds R = 0..40) show an intermediate optimal R and, in the low-R regime most relevant for near-term machines, learned residual decoding yields the lowest logical error rates and can outperform an unencoded physical two-qubit RB reference under the same noise model.
| Topic | Machine learning techniques for experimental quantum information science |
|---|