Group-Theoretic Reinforcement Learning of Dynamical Decoupling Sequences

2025-12-15

AI Summary

To address the challenge of phase decoherence suppression in qubits—where conventional dynamic decoupling (DD) requires prior knowledge of the noise spectrum and struggles with real-time pulse-sequence optimization—this work proposes a model-free reinforcement learning (RL) framework for autonomous DD sequence design and online adaptation. Innovatively, we embed the Thompson group F into the RL action space, enabling non-convex optimization tailored to bounded-length sequential decision-making, thereby eliminating reliance on known noise spectra or Gaussian noise assumptions. Experiments demonstrate that the learned DD sequences significantly suppress dephasing under unknown, non-Gaussian realistic noise, outperforming classical DD schemes. This approach establishes a scalable, robust paradigm for on-chip, real-time adaptive quantum error mitigation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Dynamical decoupling seeks to mitigate phase decoherence in qubits by applying a carefully designed sequence of effectively instantaneous electromagnetic pulses. Although analytic solutions exist for pulse timings that are optimal under specific noise regimes, identifying the optimal timings for a realistic noise spectrum remains challenging. We propose a reinforcement learning (RL)-based method for designing pulse sequences on qubits. Our novel action set enables the RL agent to efficiently navigate this inherently non-convex optimization landscape. The action set, derived from Thompson's group $F$, is applicable to a broad class of sequential decision problems whose states can be represented as bounded sequences. We demonstrate that our RL agent can learn pulse sequences that minimize dephasing without requiring explicit knowledge of the underlying noise spectrum. This work opens the possibility for real-time learning of optimal dynamical decoupling sequences on qubits which are dephasing-limited. The model-free nature of our algorithm suggests that the agent may ultimately learn optimal pulse sequences even in the presence of unmodeled physical effects, such as pulse errors or non-Gaussian noise.

Problem

• Optimizing pulse timings for realistic noise spectra
• Designing dynamical decoupling sequences using reinforcement learning
• Minimizing qubit dephasing without explicit noise knowledge

Innovation

• Reinforcement learning designs qubit pulse sequences
• Action set derived from Thompson's group F
• Model-free learning without noise spectrum knowledge