Discovering autonomous quantum error correction via deep reinforcement learning

TL;DR

This paper employs deep reinforcement learning with curriculum learning to discover optimal bosonic quantum error correction codes that outperform existing methods in resisting photon loss and noise, advancing fault-tolerant quantum computing.

Contribution

It introduces a novel approach combining analytical solutions and deep reinforcement learning to identify effective quantum error correction codes under approximate autonomous frameworks.

Findings

Discovered bosonic codes using RL that surpass the breakeven point.

Identified optimal codewords as Fock states |4> and |7>.

Achieved state-of-the-art performance in quantum error correction.

Abstract

Quantum error correction is essential for fault-tolerant quantum computing. However, standard methods relying on active measurements may introduce additional errors. Autonomous quantum error correction (AQEC) circumvents this by utilizing engineered dissipation and drives in bosonic systems, but identifying practical encoding remains challenging due to stringent Knill-Laflamme conditions. In this work, we utilize curriculum learning enabled deep reinforcement learning to discover Bosonic codes under approximate AQEC framework to resist both single-photon and double-photon losses. We present an analytical solution of solving the master equation under approximation conditions, which can significantly accelerate the training process of reinforcement learning. The agent first identifies an encoded subspace surpassing the breakeven point through rapid exploration within a constrained evolutionary time-frame, then strategically fine-tunes its policy to sustain this performance advantage over extended temporal horizons. We find that the two-phase trained agent can discover the optimal set of codewords, i.e., the Fock states $\ket{4}$ and $\ket{7}$ considering the effect of both single-photon and double-photon loss. We identify that the discovered code surpasses the breakeven threshold over a longer evolution time and achieve the state-of-art performance. We also analyze the robustness of the code against the phase damping and amplitude damping noise. Our work highlights the potential of curriculum learning enabled deep reinforcement learning in discovering the optimal quantum error correct code especially in early fault-tolerant quantum systems.