Reinforcement Learning Control of Quantum Error Correction

TL;DR

This paper introduces a reinforcement learning framework that continuously stabilizes quantum systems during computation, significantly improving error correction performance and scalability in quantum processors.

Contribution

It unifies calibration with computation by repurposing error detection as a learning signal for RL, enabling real-time stabilization and record performance in quantum error correction.

Findings

Improved logical stability of the surface code 3.5-fold against drift.

Achieved record logical error rates for surface and color codes.

Confirmed scalability of RL framework up to large code distances.

Abstract

The promise of fault-tolerant quantum computing is challenged by environmental drift that relentlessly degrades the quality of quantum operations. The contemporary solution, halting the entire quantum computation for recalibration, is unsustainable for the long runtimes of the future algorithms. We address this challenge by unifying calibration with computation, granting the quantum error correction 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 physical control parameters and stabilize the quantum system during the 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, including RL fine-tuning of the entire system and near-optimal decoding, we achieve record performance of the surface and color codes, with average logical error per cycle of $\varepsilon_L=7.72(9)\times10^{-4}$ and $\varepsilon_L=8.19(14)\times10^{-3}$ respectively. Simulations of surface codes up to distance-15 with tens of thousands control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of the system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing.