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

TL;DR

This paper introduces a novel adaptive control approach using a Chronological Deep Q-Network to manage non-stationary noise in fault-tolerant quantum computing, significantly extending logical survival time.

Contribution

It models fault-tolerant quantum computation as a stochastic control problem and develops a deep reinforcement learning method to adaptively track noise evolution and suppress error accumulation.

Findings

Ch-DQN outperforms static and recurrent baselines in simulations.

It effectively tracks noise drift and reduces hazard accumulation.

The approach extends logical survival time in quantum error correction.

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.