arXiv · 2511.08493
Reinforcement Learning Control of Quantum Error Correction
Abstract
Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. Its prerequisite is that errors must remain sufficiently rare, which requires perpetually adapting the computer's control parameters to the drifting environment 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 algorithms. We address this challenge by unifying calibration with computation. We grant the QEC process 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 (RL) agent 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 color codes, with average logical error per cycle of $7.72(9)\times10^{-4}$ and $8.19(14)\times10^{-3}$ 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.
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Volodymyr Sivak, Alexis Morvan, Michael Broughton, Rodrigo G. Cortiñas, Johannes Bausch, Andrew W. Senior, Matthew Neeley, Alec Eickbusch, Noah Shutty, Laleh Aghababaie Beni, James S. Spencer, Francisco J. H Heras, Thomas Edlich, Dmitry Abanin, Amira Abbas, Rajeev Acharya, Georg Aigeldinger, Ross Alcaraz, Sayra Alcaraz, Trond I. Andersen, Markus Ansmann, Frank Arute, Kunal Arya, Walt Askew, Nikita Astrakhantsev, Juan Atalaya, Brian Ballard, Joseph C. Bardin, Hector Bates, Andreas Bengtsson, Majid Bigdeli Karimi, Alexander Bilmes, Simon Bilodeau, Felix Borjans, Alexandre Bourassa, Jenna Bovaird, Dylan Bowers, Leon Brill, Peter Brooks, David A. Browne, Brett Buchea, Bob B. Buckley, Tim Burger, Brian Burkett, Nicholas Bushnell, Jamal Busnaina, Anthony Cabrera, Juan Campero, Hung-Shen Chang, Silas Chen, Ben Chiaro, Liang-Ying Chih, Agnetta Y. Cleland, Bryan Cochrane, Matt Cockrell, Josh Cogan, Roberto Collins, Paul Conner, Harold Cook, William Courtney, Alexander L. Crook, Ben Curtin, Martin Damyanov, Sayan Das, Dripto M. Debroy, Sean Demura, Paul Donohoe, Ilya Drozdov, Andrew Dunsworth, Valerie Ehimhen, Aviv Moshe Elbag, Lior Ella, Mahmoud Elzouka, David Enriquez, Catherine Erickson, Vinicius S. Ferreira, Marcos Flores, Leslie Flores Burgos, Ebrahim Forati, Jeremiah Ford, Austin G. Fowler, Brooks Foxen, Masaya Fukami, Alan Wing Lun Fung, Lenny Fuste, Suhas Ganjam, Gonzalo Garcia, Christopher Garrick, Robert Gasca, Helge Gehring, Robert Geiger, Élie Genois, William Giang, Dar Gilboa, James E. Goeders, Edward C. Gonzales, Raja Gosula, Stijn J. de Graaf, Alejandro Grajales Dau, Dietrich Graumann. 2026-06-19. Reinforcement Learning Control of Quantum Error Correction. https://arxiv.org/abs/2511.08493
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