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International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT)

RL-QEC: Harnessing Reinforcement Learning for Quantum Error Correction Advancements

K. M. Veeresh, Deepak S, Srinivas T

Published Mar 22, 2024• 2 citations
Publisher

Abstract

With a particular emphasis on fixing fault defects caused by bitflip and depolarizing noise, this study explores the use of RL methods within the framework of quantum error correction. This work investigates the flexibility and independence of RL-trained agents in reducing bitflip mistakes by methodically looking at error rates and qubit lifetimes. Furthermore, we study how the top-performing agents are affected by depolarizing noise and present a strategic "Decoded Surface Code" method for fixing mistakes. The results show how RL works in handling different types of errors on its own, revealing subtle dynamics in the quantum system. Quantum error correction is a rapidly developing area, and this study adds to it by shedding light on how reinforcement learning could improve quantum computing's fault tolerance. The study's findings provide a basis for future research into more robust quantum computing systems and have ramifications for the creation of autonomous error-correcting algorithms.

DOI
10.1109/tqcebt59414.2024.10545200

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With a particular emphasis on fixing fault defects caused by bitflip and depolarizing noise, this study explores the use of RL methods within the framework of quantum error correction. This work investigates the flexibility and independence of RL-trained agents in reducing bitflip mistakes by methodically looking at error rates and qubit lifetimes. Furthermore, we study how the top-performing agents are affected by depolarizing noise and present a strategic "Decoded Surface Code" method for fixing mistakes. The results show how RL works in handling different types of errors on its own, revealing subtle dynamics in the quantum system. Quantum error correction is a rapidly developing area, and this study adds to it by shedding light on how reinforcement learning could improve quantum computing's fault tolerance. The study's findings provide a basis for future research into more robust quantum computing systems and have ramifications for the creation of autonomous error-correcting algorithms.
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Quantum error correction (QEC) is the primary strategy for protecting a quantum computer from the environment. Its prerequisite is that errors must remain sufficiently rare, which requires perpetually adapting the computer's control parameters to the drifting environment conditions. The current solution to this problem is to terminate the entire quantum computation for recalibration, but it is incompatible with the long runtimes of future quantum algorithms. We address this challenge by unifying calibration with computation. We grant the QEC process a dual role: its error detection events are not only used to correct the logical quantum state, but are also repurposed as a learning signal, teaching a reinforcement learning (RL) agent to continuously steer the control parameters and stabilize the quantum system during computation. We experimentally demonstrate this framework on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold against injected drift. By synthesizing our full suite of technological advances, we achieve record performance of the surface and color codes, with average logical error per cycle of 7.72(9)×10−47.72(9)\times10^{-4}7.72(9)×10−4 and 8.19(14)×10−38.19(14)\times10^{-3}8.19(14)×10−3 respectively. Numerical simulations of large codes with tens of thousands of control parameters confirm the scalability of our RL framework, revealing an optimization speed that is independent of system size. This work thus enables a new paradigm: a quantum computer that learns from its errors and never stops computing.
Quantum error correction (QEC) is indispensable for the realization of fault-tolerant quantum computers. Surface codes are a leading QEC architecture due to their high error thresholds and planar design. The core challenge lies in the decoding process: inferring latent physical errors from syndrome measurements and applying appropriate corrections. Although there are classical decoders, their performance can degrade with complex noise or be computationally demanding. This paper investigates an advanced Deep Reinforcement Learning (DRL) approach, employing a Dueling Double Deep Q-Network (DQN), to decode X-type Pauli errors on ad=3\mathbf{d}=\mathbf{3}d=3rotated surface code. The DRL agent leverages syndrome history to discern temporal error correlations and utilizes a multi-round correction strategy, enabling iterative refinement of corrective actions within an environment that simulates both physical qubit errors and syndrome measurement noise. After 30,000 training episodes with a 5 % physical error rate and 1 % measurement noise, the agent achieved a 0.00 % logical error rate in the extended test, an average episode reward of 0.38, and successfully cleared syndromes in 63 % of the test cases, demonstrating robust learning of a safe decoding policy. The persistent challenge of high training loss, despite good policy performance, is discussed, offering insights into value function approximation in complex QEC environments.
This paper presents a novel approach to quantum error correction (QEC) leveraging reinforcement learning (RL). The core idea is to develop an algorithm capable of dynamically learning and adapting QEC code parameters based on the evolving noise environment. Traditional QEC codes are often pre-defined and may not optimally perform in all scenarios. This research proposes a system where network nodes autonomously adjust the code parameters using an RL framework, reacting to error detection results and feedback signals. The system's ability to self-optimize based on real-time noise characteristics represents a significant advancement in QEC design. The key objective is to enhance error correction performance by adapting to the dynamic nature of quantum noise. The theoretical framework and the proposed algorithm are detailed within this document, highlighting the potential for improved resilience in quantum communication and computation.
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 ∣4⟩\ket{4}∣4⟩ and ∣7⟩\ket{7}∣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.
The practical realization of fault-tolerant quantum computing depends critically on the efficiency and adaptability of quantum error correction (QEC) decoders. Recent advancements have highlighted deep reinforcement learning (DRL) as a promising approach for decoding topological codes. However, training these agents typically assumes simplified, uniform noise models, which fail to capture the complex, compounding errors characteristic of near-term quantum hardware. In this paper, we rigorously formulate the synthesis of compound error models—encompassing thermal relaxation, spatial cross-talk, and non-Markovian memory effects—within the framework of deep reinforcement learning for surface code decoding. We propose a methodological framework for simulating these mismatch environments during training to foster robust policy generalization. Extensive simulations demonstrate that DRL agents trained on our synthesized compound error models achieve up to a 24%24\%24% reduction in logical error rates under realistic noise conditions compared to agents trained on standard depolarizing noise. This work provides a rigorous foundation for synthesizing accurate quantum noise environments for reinforcement learning applications.