July 22, 2026 Volodymyr Sivak and Paul Klimov, Research Scientists, Google Quantum AI, Google Research By integrating reinforcement learning with quantum error correction, we showed that a quantum computer can continuously adapt to drift and remain stable during long computations. Imagine a symphony orchestra performing a complex masterpiece. If the violins drifted out of tune every few measures, the ensemble would constantly have to stop and retune their instruments. Thankfully, this doesn't happen in an orchestra because the instruments reliably stay in tune. However, it is the current reality of operating a quantum computer. Since quantum computers are fundamentally analog machines that are sensitive to drift, maintaining reliable operation requires perpetually recalibrating their control parameters, i.e., the frequencies, amplitudes, and phases of the analog signals choreographing the qubits. Today, this requires fully terminating the entire quantum computation. This complete decoupling of computation and calibration represents a fundamental bottleneck for the future, as useful quantum algorithms must run continuously for days or even months. To address this, in “Reinforcement learning control of quantum error correction”, published in Nature, we demonstrated a reinforcement learning (RL) framework in which an autonomous agent learns from quantum error detections to continuously steer thousands of control parameters, stabilizing the quantum system against drift during the computation. In short: we found a way to tune the instruments while the music plays. In a concert hall, a detuned instrument is immediately heard. The quantum realm offers no such luxury. As if the very act of listening ruined the performance, measuring the qubits collapses their quantum superposition states. To preserve the quantum information, we instead employ Quantum Error Correction (QEC), a technique that exploits redundancy to create “logical qubits” out of many physical qubits, and uses specialized parity checks on the physical qubits to digitize the analog noise into