迈向能够从错误中学习的量子计算机

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 binary error detection events. Unfortunately, these bits only tell us that an error occurred somewhere within a bounded spacetime region of the quantum circuit, not its exact location. It is like hearing a sour note without knowing exactly which musician played it. To pinpoint the likely error locations and calculate the necessary corrections, we rely on QEC decoders, such as the neural network decoder AlphaQubit (trained on real data) and algorithmic decoder Tesseract . If errors are sufficiently rare, these decoders can successfully restore the logical quantum information by analyzing the error detection data. However, decoders leave a crucial question unanswered: why did those errors happen in the first place? Some errors result from the unavoidable interaction of a quantum system with its surrounding environment, leading to decoherence . This ruthless process destroys macroscopic quantum superpositions, effectively turning quantum computers into classical ones. This fundamental phenomenon is so pervasive that it causes our familiar classical reality to emerge from the underlying quantum laws of Nature. While these environmental errors can never be completely prevented, many others are manifestations of imprecise control calibration and hardware drift – flaws that remain within our power to mitigate. Traditionally, quantum calibration relied on physics models. Its techniques were refined through decades of quantum control research. However, across technological domains, human-crafted models inevitably hit a performance ceiling. Early computer vision stalled when relying on strict geometric rules. Traditional robotics still struggles with kinematic equations that fail to capture the messy reality of contact dynamics and friction. Similarly, the decades-old challenge of predicting protein folding remained largely intractable for traditional physical models until deep learning systems l