Approximate Autonomous Quantum Error Correction with Reinforcement Learning
Yexiong Zeng, Zheng-Yang Zhou, Enrico Rinaldi, Clemens Gneiting,, Franco Nori

TL;DR
This paper introduces a novel approximate autonomous quantum error correction scheme using reinforcement learning to identify effective bosonic codewords, achieving error suppression with reduced Hamiltonian complexity.
Contribution
It proposes a new bosonic code for AQEC based on RL-optimized codewords, relaxing Knill-Laflamme conditions and simplifying implementation.
Findings
RL-optimized codewords are 2 angle and 4 angle
Successfully suppresses single-photon loss, surpassing break-even threshold
Reduces Hamiltonian distance to 1 for error correction Hamiltonian
Abstract
Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances . Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum Mechanics and Applications
