Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning

• URL: http://arxiv.org/abs/2305.06378v2
• Date: Mon, 12 Jun 2023 18:51:09 GMT
• Title: Discovery of Optimal Quantum Error Correcting Codes via Reinforcement Learning
• Authors: Vincent Paul Su, ChunJun Cao, Hong-Ye Hu, Yariv Yanay, Charles Tahan, Brian Swingle
• Abstract summary: The recently introduced Quantum Lego framework provides a powerful method for generating complex quantum error correcting codes. We gamify this process and unlock a new avenue for code design and discovery using reinforcement learning (RL) We train on two such properties, maximizing the code distance, and minimizing the probability of logical error under biased Pauli noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recently introduced Quantum Lego framework provides a powerful method for generating complex quantum error correcting codes (QECCs) out of simple ones. We gamify this process and unlock a new avenue for code design and discovery using reinforcement learning (RL). One benefit of RL is that we can specify \textit{arbitrary} properties of the code to be optimized. We train on two such properties, maximizing the code distance, and minimizing the probability of logical error under biased Pauli noise. For the first, we show that the trained agent identifies ways to increase code distance beyond naive concatenation, saturating the linear programming bound for CSS codes on 13 qubits. With a learning objective to minimize the logical error probability under biased Pauli noise, we find the best known CSS code at this task for $\lesssim 20$ qubits. Compared to other (locally deformed) CSS codes, including Surface, XZZX, and 2D Color codes, our $[[17,1,3]]$ code construction actually has \textit{lower} adversarial distance, yet better protects the logical information, highlighting the importance of QECC desiderata. Lastly, we comment on how this RL framework can be used in conjunction with physical quantum devices to tailor a code without explicit characterization of the noise model.

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