Adaptive Control of Stochastic Error Accumulation in Fault-Tolerant Quantum Computation

• URL: http://arxiv.org/abs/2603.14687v1
• Date: Mon, 16 Mar 2026 00:46:00 GMT
• Title: Adaptive Control of Stochastic Error Accumulation in Fault-Tolerant Quantum Computation
• Abstract summary: Non-stationary noise and drift lead to logical failure from the temporal accumulation of errors, not from independent events.<n>A Chronological Deep Q-Network (Ch-DQN) maintains an internal belief state that tracks both noise evolution and accumulated hazard.<n>A fractional meta-update stabilizes learning in the presence of non-stationary, control-coupled dynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In realistic hardware for quantum computation that possesses fault-tolerance, non-stationary noise and stochastic drift lead to logical failure from the temporal accumulation of errors, not from independent events. Static decoding and fixed calibration techniques are structurally incompatible with this situation because they do not take into account temporal correlations between errors or control-induced back-action of errors. These effects motivate control policies that must track noise evolution across correction cycles, rather than respond to individual syndromes in isolation. We treat fault-tolerant quantum computation as a stochastic control problem, modelled using reduced quantum dynamics in which Pauli error processes are governed by latent noise parameters that vary temporally. From this perspective, logical failure arises through the accumulation of a hazard variable, and the corresponding control objective depends on the full history of observations. Operating under these conditions, a Chronological Deep Q-Network (Ch-DQN) maintains an internal belief state that tracks both noise evolution and accumulated hazard. During training, backward refinement of trajectories is used to sample slowly drifting modes of operation, while runtime inference remains strictly causal. A fractional meta-update stabilizes learning in the presence of non-stationary, control-coupled dynamics. Through multi-distance simulations that incorporate stochastic drift and feedback from decision-making, Ch-DQN suppresses hazard accumulation and extends logical survival time relative to static and recurrent baselines. Error correction in this regime is therefore no longer a static decoding task, but a control process whose success is determined over time by the underlying noise dynamics.

Related papers

• Extrapolative Quantum Error Mitigation in Continuous-Variable Systems beyond the Training Horizon [17.211388118859354]
  We introduce a framework for extrapolative quantum error mitigation based on a time-conditioned Swin Transformer.<n>Our results establish extrapolative QEM as a practical route to mitigating noise in CV quantum systems without exhaustive training data.
  arXiv  Detail & Related papers  (2026-03-09T16:14:17Z)
• Entropy Production in Machine Learning Under Fokker-Planck Probability Flow [0.0]
  We propose an entropy-based retraining framework grounded in non-equilibrium cost dynamics.<n>We show that entropy-triggered retraining achieves predictive performance comparable to high-frequency retraining.
  arXiv  Detail & Related papers  (2026-01-02T04:01:57Z)
• Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming [0.3848364262836075]
  This work proposes a unified control architecture that couples a Reinforcement Learning (RL)-driven controller with a disturbance-rejection Extended State Observer (ESO)<n>The ESO is utilized to estimate the system states and the lumped disturbance in real time, forming the foundation for effective disturbance compensation.
  arXiv  Detail & Related papers  (2025-12-05T22:52:22Z)
• Reinforcement Learning Control of Quantum Error Correction [108.70420561323692]
  Quantum computer learns to self-improve directly from its errors and never stops computing.<n>This work enables a new paradigm: a quantum computer that learns to self-improve directly from its errors and never stops computing.
  arXiv  Detail & Related papers  (2025-11-11T17:32:25Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Error-quantified Conformal Inference for Time Series [55.11926160774831]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.<n>We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function.<n>ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Limitations to Dynamical Error Suppression and Gate-Error Virtualization from Temporally Correlated Nonclassical Noise [0.0]
  We study a minimal exactly solvable single-qubit model under Gaussian quantum dephasing noise.<n>For digital periodic control, we prove that the gate fidelity saturates at a value that is strictly smaller than the one attainable in the absence of control history.<n>We find that only if decoupling can keep the qubit highly pure over a timescale larger than the correlation time of the noise, the bath approximately converges to its original statistics.
  arXiv  Detail & Related papers  (2024-07-05T18:00:00Z)
• Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions [59.605246463200736]
  We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. We use state-of-the-art verification techniques to provide guarantees on the interval Markov decision process and compute a controller for which these guarantees carry over to the original control system.
  arXiv  Detail & Related papers  (2023-01-04T10:40:30Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Measurement based estimator scheme for continuous quantum error correction [52.77024349608834]
  Canonical discrete quantum error correction (DQEC) schemes use projective von Neumann measurements on stabilizers to discretize the error syndromes into a finite set. Quantum error correction (QEC) based on continuous measurement, known as continuous quantum error correction (CQEC), can be executed faster than DQEC and can also be resource efficient. We show that by constructing a measurement-based estimator (MBE) of the logical qubit to be protected, it is possible to accurately track the errors occurring on the physical qubits in real time.
  arXiv  Detail & Related papers  (2022-03-25T09:07:18Z)
• Error mitigation via stabilizer measurement emulation [0.0]
  We introduce and demonstrate quantum measurement emulation (QQME) QQME effectively emulates the measurement of stabilizer operators via gate application, leading to a first-order insensitivity to coherent errors. It is particularly well-suited to discrete coherent errors that are challenging for DD to address.
  arXiv  Detail & Related papers  (2021-02-10T22:58:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.