外观
论文arXiv 2610.2198713 天前
通过强化学习实现固态量子比特的量子控制
原文链接：Quantum control of solid state qubits via Reinforcement Learning（arXiv 2610.21987 · 发布日期：2026-10-15）
Quantum control of solid state qubits via Reinforcement Learning
本文用仿真分析无模型强化学习在自旋浴中NV核自旋间接控制的态制备与量子门设计表现。
核心要点
- 无模型强化学习可作为黑盒优化工具，用于噪声NISQ量子设备的脉冲级控制策略设计。
- 研究聚焦自旋浴中NV核自旋的间接控制，面向态制备和量子比特门设计。
- 自旋浴通过广义簇关联展开建模，考虑磁性杂质和浴内相互作用。
- 在最小可观测环境与含噪稀疏奖励下仿真评估RL智能体，并与其他先进黑盒优化工具比较。
验证范围与局限：验证范围为仿真分析，场景是自旋浴中NV核自旋的间接控制。
英文摘要 (Abstract)
The use of reinforcement learning (RL) in the design of quantum control strategies for pulse level control of NISQ era quantum devices is attracting growing interest. Model-free RL, not requiring preexisting knowledge of the physical system, acts as a black-box optimization tool well suited for noisy devices. In this work, we provide a simulation-based analysis of the performance of model-free RL for state preparation and qubit gate design in a challenging setup: indirect control of NV nuclear spins in a spin-bath. In particular, the spin-bath is modeled via the generalized cluster correlation expansion accounting for magnetic impurities and intra-bath interactions. We analyze the performance of the RL agents by performing simulations with minimally observable environments and noisy, sparse rewards as introduced in (Phys. Rev. X 12, 011059), and compare them to other state-of-the-art black-box optimization tools.
作者：Dennis Herb, Mirko Rossini, Sabine Wölk, Joachim Ankerhold, Dominik Maile, Alessio Belenchia
来源
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