Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment1,2. The prerequisite of QEC is that errors must remain sufficiently rare, which requires perpetually adapting the control parameters of the computer to the drifting environmental conditions. The current solution to this problem is to terminate the entire quantum computation for recalibration, but it is incompatible with the long runtimes of future quantum algorithms3,4. Here we address this challenge by unifying calibration with computation. We grant the QEC process5, 6, 7, 8, 9, 10–11 a dual role: its error-detection events are not only used to correct the logical quantum state but are also repurposed as a learning signal, teaching a reinforcement learning agent12, 13, 14, 15–16 to continuously steer the control parameters and stabilize the quantum system during computation. We experimentally demonstrate this framework on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. By synthesizing our full suite of technological advances, we achieve record performance of the surface and colour codes, with average logical error per cycle of 7.72(9) × 10−4 and 8.19(14) × 10−3, respectively. Numerical simulations of large codes with tens of thousands of control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing. By integrating reinforcement learning with quantum error correction, a quantum computer continuously self-calibrates during computation, achieving record logical error rates and enhanced resilience to drift.
V. Sivak, A. Morvan, M. Broughton et al.· Nature· 0 citations
Quantum error correction (QEC) will likely be required to realize the full potential of quantum computing, but comes with daunting hardware overheads and demands low gate errors on the physical qubits1, 2, 3–4. These requirements can be eased by engineering qubits with a strong error hierarchy, in which the most common noise channels are also the easiest to correct. Erasure qubits can achieve this when detectable leakage errors out of the computational subspace dominate over the residual Pauli errors5, 6, 7, 8, 9, 10–11, resulting in higher thresholds and improved scaling with code distance5,12,13. In practice, these advantages come to fruition only if the error hierarchy is preserved as much as possible throughout all gates and operations. Here we design and realize a two-qubit entangling gate for dual-rail cavity qubits, a type of erasure qubit encoded in a pair of superconducting microwave cavities7. Our experimental demonstration confirms that the error hierarchy is largely preserved during the gate. The gate is fast (about 500 ns duration) and shows low erasure rates of approximately 0.5% per gate, remaining Pauli errors below 0.1%, and a strong bias towards dephasing errors, in which bit-flips are practically non-existent at the 10−6 level. These results enable a faster path to error-corrected systems that rapidly suppress errors as they scale; a claim we support with our detailed surface code simulations. A fast, low-error entangling gate for dual-rail cavity erasure qubits preserves a strong error hierarchy, advancing scalable quantum error correction with substantially improved fault-tolerant performance.
Nitish James D. Taewan Ankur Amos Beau Avadh Winfred Anth Mehta Teoh Noh Agrawal Anderson Birdsall Brahmbhat, Nitish Mehta, James D. Teoh et al.· Nature· 0 citations
