Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Accelerating searching for quantum error correction codes with reinforcement learning

Evan E. Dobbs

Published Dec 31, 2025• 0 citations
OpenAlex

Abstract

Quantum Error Correction (QEC) codes are a necessary component of utility-scale quantum computers, and the discovery of new codes with desirable properties is one of the most promising avenues for reducing the estimated resources required to perform useful quantum computations. Most current methods for code discovery rely on analytical techniques and construct codes from algebraic or geometric structures, but there also exist some techniques for discovering codes via computational search. In this work we focus on one of the most promising of these methods where a reinforcement learning agent is trained to construct encoding circuits and evaluates the performance of the corresponding code. We seek to improve upon this method by reducing the computational resources required to discover codes of given parameters, primarily by utilizing a probabilistic approximation of the reward function which relies on randomly sampling error operators according to some specified distribution. We investigate the performance of our modified framework and compare the discovered codes and required computational resources to the original work, concluding that probabilistic methods such as our sampling technique are effective strategies for reducing the computational cost of the evaluation component of code searching without excessive penalties to performance.

Related papers

Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding

Yidong Zhou, Lingyi Kong, Yifeng Peng +1 more

The advent of promising quantum error correction (QEC) codes with efficient resource utilization and high-performance fault-tolerant quantum memories signifies a critical step towards realizing practical quantum computation. While surface codes have been a dominant approach, their limitations have spurred the development of more advanced QEC architectures. These advanced codes often present increased complexity, demanding innovative decoding methodologies. This work investigates the application of reinforcement learning (RL) techniques, including hybrid and multi-agent approaches, to enhance the decoding of various advanced QEC architectures. By leveraging the ability of RL to learn optimal strategies from noisy syndrome measurements, we explore the potential for achieving improved logical error rates and scalability compared to traditional decoding methods. Our approach examines the adaptation of reinforcement learning to exploit the structural properties of these modern QEC models. We also explore the benefits of combining different RL algorithms to address the multifaceted nature of the decoding problem, considering factors such as code degeneracy and real-world noise characteristics. With our proposed method, we are able to demonstrate that an autonomously trained agent can derive decoding schemes for the complex decoding requirement of advanced QEC architectures.

quant-pharXiv:2601.19279•Jan 28, 2026•0 citations

Simultaneous discovery of quantum error correction codes and encoders with a noise-aware reinforcement learning agent

Jan Ollé, Remmy A. M. Zen, Matteo Ballerini Puviani +1 more

Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.

arXiv:2311.04750•npj Quantum Information•Dec 3, 2024•24 citations

Discovering autonomous quantum error correction via deep reinforcement learning

Yue Yin, Tailong Xiao, Xiaoyang Deng +3 more

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.

quant-pharXiv:2511.12482•Physical Review A•Nov 18, 2025•0 citations

Adaptive Quantum Error Correction via Reinforcement Learning

Jincheng Zhang

Quantum error correction (QEC) is a crucial element in the realization of fault-tolerant quantum computation. However, traditional QEC schemes often rely on pre-determined, static parameters, which may not be optimally suited for the specific noise characteristics present in a given quantum hardware. This work introduces a novel approach to QEC that leverages reinforcement learning (RL) to dynamically adapt the error correction protocol in real-time. The system learns to optimize the parameters of the QEC code, such as measurement schedules and decoding strategies, based on continuous feedback from the quantum hardware. This adaptive approach promises to significantly improve the performance and efficiency of QEC, reducing the overhead associated with error correction and enhancing the fidelity of quantum computations. The core claim of this paper is that quantum error correction is computationally intensive and requires precise control over quantum systems. The central mechanism involves employing reinforcement learning to optimize the parameters of quantum error correction protocols in real-time, adapting to the specific noise characteristics of the quantum hardware. This represents a new automation of the QEC process.

•Sep 7, 2026•0 citations

Reinforcement Learning Control of Quantum Error Correction

Volodymyr Sivak, Alexis Morvan, Michael Broughton +296 more

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 $7.72(9)\times10^{-4}$ and $8.19(14)\times10^{-3}$ 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.

quant-pharXiv:2511.08493•Nature•Nov 12, 2025•5 citations

Discovery of optimal quantum codes via reinforcement learning

Vincent Paul Su, ChunJun Cao, Hong-Ye Hu +3 more

The design and optimization of quantum codes is generally hard because of the prohibitive computational cost. 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). Already for simple code measures and modest qubit numbers, we produce codes that are both optimal and novel. Moreover, we argue that this framework is both scalable, when combined with efficient tensor contraction methods, and flexible, since we can specify 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 (Calderbank, Shor, Steane) 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 $\ensuremath{\lesssim}20$ qubits. Compared to other (locally deformed) CSS codes, including Surface, XZZX, and two-dimensional Color codes, our $[[17,1,3]]$ code construction actually has 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.

arXiv:2305.06378•Physical Review Applied•Mar 20, 2025•11 citations
Quantum Error Correction (QEC) codes are a necessary component of utility-scale quantum computers, and the discovery of new codes with desirable properties is one of the most promising avenues for reducing the estimated resources required to perform useful quantum computations. Most current methods for code discovery rely on analytical techniques and construct codes from algebraic or geometric structures, but there also exist some techniques for discovering codes via computational search. In this work we focus on one of the most promising of these methods where a reinforcement learning agent is trained to construct encoding circuits and evaluates the performance of the corresponding code. We seek to improve upon this method by reducing the computational resources required to discover codes of given parameters, primarily by utilizing a probabilistic approximation of the reward function which relies on randomly sampling error operators according to some specified distribution. We investigate the performance of our modified framework and compare the discovered codes and required computational resources to the original work, concluding that probabilistic methods such as our sampling technique are effective strategies for reducing the computational cost of the evaluation component of code searching without excessive penalties to performance.
The advent of promising quantum error correction (QEC) codes with efficient resource utilization and high-performance fault-tolerant quantum memories signifies a critical step towards realizing practical quantum computation. While surface codes have been a dominant approach, their limitations have spurred the development of more advanced QEC architectures. These advanced codes often present increased complexity, demanding innovative decoding methodologies. This work investigates the application of reinforcement learning (RL) techniques, including hybrid and multi-agent approaches, to enhance the decoding of various advanced QEC architectures. By leveraging the ability of RL to learn optimal strategies from noisy syndrome measurements, we explore the potential for achieving improved logical error rates and scalability compared to traditional decoding methods. Our approach examines the adaptation of reinforcement learning to exploit the structural properties of these modern QEC models. We also explore the benefits of combining different RL algorithms to address the multifaceted nature of the decoding problem, considering factors such as code degeneracy and real-world noise characteristics. With our proposed method, we are able to demonstrate that an autonomously trained agent can derive decoding schemes for the complex decoding requirement of advanced QEC architectures.
Abstract In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.
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.
Quantum error correction (QEC) is a crucial element in the realization of fault-tolerant quantum computation. However, traditional QEC schemes often rely on pre-determined, static parameters, which may not be optimally suited for the specific noise characteristics present in a given quantum hardware. This work introduces a novel approach to QEC that leverages reinforcement learning (RL) to dynamically adapt the error correction protocol in real-time. The system learns to optimize the parameters of the QEC code, such as measurement schedules and decoding strategies, based on continuous feedback from the quantum hardware. This adaptive approach promises to significantly improve the performance and efficiency of QEC, reducing the overhead associated with error correction and enhancing the fidelity of quantum computations. The core claim of this paper is that quantum error correction is computationally intensive and requires precise control over quantum systems. The central mechanism involves employing reinforcement learning to optimize the parameters of quantum error correction protocols in real-time, adapting to the specific noise characteristics of the quantum hardware. This represents a new automation of the QEC process.
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.
The design and optimization of quantum codes is generally hard because of the prohibitive computational cost. 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). Already for simple code measures and modest qubit numbers, we produce codes that are both optimal and novel. Moreover, we argue that this framework is both scalable, when combined with efficient tensor contraction methods, and flexible, since we can specify 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 (Calderbank, Shor, Steane) 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 $\ensuremath{\lesssim}20$ qubits. Compared to other (locally deformed) CSS codes, including Surface, XZZX, and two-dimensional Color codes, our [[17,1,3]][[17,1,3]][[17,1,3]] code construction actually has 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.