Towards a quantum computer that learns from its errors

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.

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