Google · Google Research
Towards a quantum computer that learns from its errors
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Volodymyr Sivak and Paul Klimov, Research Scientists, Google Quantum AI, Google Research.
Key facts
- Volodymyr Sivak and Paul Klimov, Research Scientists, Google Quantum AI, Google Research
- Google Research has a rich history of pioneering RL to solve problems too complex for traditional programming
- Recently, AlphaQubit surpassed the accuracy of the most powerful algorithmic QEC decoders
- Since QEC already generates a steady stream of detection events, they simply granted this data a complementary role
Summary
By integrating reinforcement learning with quantum error correction, they showed that a quantum computer can continuously adapt to drift and remain stable during long computations. Imagine a symphony orchestra performing a complex masterpiece. 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. To address this, in “ Reinforcement learning control of quantum error correction ”, published in Nature, they 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.