Autonomous tuning of particle accelerators is an active and challenging field
of research with the goal of enabling novel accelerator technologies
cutting-edge high-impact applications, such as physics discovery, cancer
research and material sciences. A key challenge with autonomous accelerator
tuning remains that the most capable algorithms require an expert in
optimisation, machine learning or a similar field to implement the algorithm
for every new tuning task. In this work, we propose the use of large language
models (LLMs) to tune particle accelerators. We demonstrate on a
proof-of-principle example the ability of LLMs to successfully and autonomously
tune a particle accelerator subsystem based on nothing more than a natural
language prompt from the operator, and compare the performance of our LLM-based
solution to state-of-the-art optimisation algorithms, such as Bayesian
optimisation (BO) and reinforcement learning-trained optimisation (RLO). In
doing so, we also show how LLMs can perform numerical optimisation of a highly
non-linear real-world objective function. Ultimately, this work represents yet
another complex task that LLMs are capable of solving and promises to help
accelerate the deployment of autonomous tuning algorithms to the day-to-day
operations of particle accelerators.

利用大型语言模型 (LLMs) 来自动调谐粒子加速器的能力，通过自然语言提示将粒子加速器的子系统调谐到最佳状态，并与贝叶斯优化 (BO) 和强化学习训练优化 (RLO) 算法进行性能比较。该研究的目标是加快自动调谐算法在粒子加速器的日常操作中的实际应用。

大型语言模型用于自然语言人机协作粒子加速器调谐

Large Language Models for Human-Machine Collaborative Particle  Accelerator Tuning through Natural Language

Spanning over two decades, the study of qubits in semiconductors for quantum
computing has yielded significant breakthroughs. However, the development of
large-scale semiconductor quantum circuits is still limited by challenges in
efficiently tuning and operating these circuits. Identifying optimal operating
conditions for these qubits is complex, involving the exploration of vast
parameter spaces. This presents a real 'needle in the haystack' problem, which,
until now, has resisted complete automation due to device variability and
fabrication imperfections. In this study, we present the first fully autonomous
tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a
clear indication of successful qubit operation. We demonstrate this automation,
achieved without human intervention, in a Ge/Si core/shell nanowire device. Our
approach integrates deep learning, Bayesian optimization, and computer vision
techniques. We expect this automation algorithm to apply to a wide range of
semiconductor qubit devices, allowing for statistical studies of qubit quality
metrics. As a demonstration of the potential of full automation, we
characterise how the Rabi frequency and g-factor depend on barrier gate
voltages for one of the qubits found by the algorithm. Twenty years after the
initial demonstrations of spin qubit operation, this significant advancement is
poised to finally catalyze the operation of large, previously unexplored
quantum circuits.

我们在这项研究中展示了第一个完全自治的半导体量子位的调谐，通过深度学习、贝叶斯优化和计算机视觉技术，实现了从基态到 Rabi 振荡的自动化，这标志着量子位成功运行。我们预计这种自动化算法可以应用于广泛的半导体量子位设备，允许对量子位质量指标进行统计研究，这将推动大规模、未被探索的量子电路的运行。

自主调谐自旋量子位

Fully autonomous tuning of a spin qubit

Online tuning of real-world plants is a complex optimisation problem that
continues to require manual intervention by experienced human operators.
Autonomous tuning is a rapidly expanding field of research, where
learning-based methods, such as Reinforcement Learning-trained Optimisation
(RLO) and Bayesian optimisation (BO), hold great promise for achieving
outstanding plant performance and reducing tuning times. Which algorithm to
choose in different scenarios, however, remains an open question. Here we
present a comparative study using a routine task in a real particle accelerator
as an example, showing that RLO generally outperforms BO, but is not always the
best choice. Based on the study's results, we provide a clear set of criteria
to guide the choice of algorithm for a given tuning task. These can ease the
adoption of learning-based autonomous tuning solutions to the operation of
complex real-world plants, ultimately improving the availability and pushing
the limits of operability of these facilities, thereby enabling scientific and
engineering advancements.

使用反馈强化学习优化（RLO）和贝叶斯优化（BO）进行比较研究，在实际粒子加速器任务中，发现 RLO 通常表现更优，但并非在所有情况下都是最佳选择。基于研究结果，提供了一组明确的标准，以指导选择给定调谐任务的算法。