MEMORY INDUSTRY INTELLIGENCE

Towards a quantum computer that learns from its errors

초안 발행 2026.07.23 03:40 사건 미확인수집 2026.10.09 22:22원문 언어 en

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v1 · 10.09 20:26 v2 · 10.09 22:22

Machine Intelligence

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초안원문 v2

we demonstrated a [reinforcement learning](https://en.wikipedia.org/wiki/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.

초안원문 v2

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.

초안원문 v2

we instead employ [Quantum Error Correction](https://research.google/blog/making-quantum-error-correction-work/) (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.

초안원문 v2

By deliberately injecting artificial drift of control parameters, we showed that RL steering improved the logical stability of our error-correcting code 3.5-fold, prolonging the time during which the processor acts as a reliable “quantum memory” device.

초안원문 v2

Yet, even after this exhaustive expert calibration, subsequent RL fine-tuning systematically suppressed the logical error rate by an additional 20%.

초안원문 v2

The synthesis of all our technologies in this experiment reduced the logical errors in quantum memories to a record low: fewer than one per thousand error correction cycles in the [surface code](https://research.google/blog/making-quantum-error-correction-work/), and one per hundred in the [color code](https://research.google/blog/a-colorful-quantum-future/).

초안원문 v2

The simulations confirmed our expectation: the number of required RL training iterations (epochs) is independent of the system size, owing to the local sensitivity of the QEC detection events to errors.

초안원문 v2

However, realizing the full potential of the RL framework requires tighter integration. By speeding up the communication cycle between the agent and the quantum processor, and employing more advanced machine learning methods, we hope to unlock significant additional improvements.

초안원문 v2

Some errors result from the unavoidable interaction of a quantum system with its surrounding environment, leading to [decoherence](https://physicstoday.aip.org/features/decoherence-and-the-transition-from-quantum-to-classical). This ruthless process destroys macroscopic quantum superpositions, effectively turning quantum computers into classical ones.

초안원문 v2

Recently, [AlphaQubit](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphaqubit-quantum-error-correction/) surpassed the accuracy of the most powerful algorithmic QEC decoders. Now quantum control faces the same ceiling. As quantum processors improve through progress in fabrication and hardware, their errors become dominated by complex phenomena that are challenging for traditional modeling and calibration.

초안원문 v2

In addition to decoding it and correcting the errors, we employ the detection events as an active learning signal. As computation progresses, the RL agent monitors this data and learns to dynamically steer the control parameters, counteracting drift and preventing new errors.

초안원문 v2

Our work thus enables a new paradigm: a quantum computer that learns from its errors and doesn’t stop computing.