Quantum feedback control with a transformer neural network architecture (2411.19253v1)

Abstract: Attention-based neural networks such as transformers have revolutionized various fields such as natural language processing, genomics, and vision. Here, we demonstrate the use of transformers for quantum feedback control through a supervised learning approach. In particular, due to the transformer's ability to capture long-range temporal correlations and training efficiency, we show that it can surpass some of the limitations of previous control approaches, e.g.~those based on recurrent neural networks trained using a similar approach or reinforcement learning. We numerically show, for the example of state stabilization of a two-level system, that our bespoke transformer architecture can achieve unit fidelity to a target state in a short time even in the presence of inefficient measurement and Hamiltonian perturbations that were not included in the training set. We also demonstrate that this approach generalizes well to the control of non-Markovian systems. Our approach can be used for quantum error correction, fast control of quantum states in the presence of colored noise, as well as real-time tuning, and characterization of quantum devices.

• The paper introduces transformer neural networks to provide rapid and efficient real-time feedback control in quantum systems.
• The study shows that transformers achieve unit fidelity in state stabilization for a two-level system despite measurement inefficiencies and system perturbations.
• Comparative analysis reveals transformer-based methods offer inference times nearly 100x faster than conventional proportional algorithms.

Quantum Feedback Control with Transformer Neural Network Architecture

In the burgeoning field of quantum feedback control, the utilization of machine learning methods has enabled significant advances in tackling the complexities inherent in quantum systems. The paper "Quantum feedback control with a transformer neural network architecture" presents a noteworthy exploration of implementing transformer neural networks for efficient quantum feedback control, demonstrating a departure from traditional methods primarily reliant on recurrent neural networks (RNNs) or reinforcement learning.

The research highlights the applicability of transformer models, well-known for their success in processing sequential data across domains such as natural language processing and genomics, to quantum feedback control tasks. The paper emphasizes that transformers, through their intrinsic design and attention mechanisms, effectively manage long-range temporal dependencies and avoid the pitfalls typical of RNN structures, such as vanishing gradients or inadequate scaling with measurement record length.

A significant aspect of this research is the demonstration of numerical stability and fidelity achieved by transformers specifically designed for the task of state stabilization in a two-level system. The transformer model was not only able to reach unit fidelity with the target quantum state swiftly but also maintained high performance levels in environments with measurement inefficiencies and unforeseen perturbations in system Hamiltonians. These outcomes illustrate the high adaptability and generalization abilities of the proposed model, significant improvements over existing techniques.

The paper explores technical details, showcasing how the custom transformer architecture incorporates both encoder and decoder components. The encoder processes the initial state and measurement record, while the decoder uses this encoded context to predict optimal feedback parameters. This design facilitates real-time application, significantly reducing computation time compared to methods like modified proportional and quantum state (PaQS) algorithms, as reflected in the comparative inference time data, where transformer-based methods outperform traditional approaches by approximately two orders of magnitude.

Furthermore, the research extends to illustrating the transformer's capability in non-Markovian dynamics, a domain where traditional feedback mechanisms struggle due to prolonged dependency on historical data. The results indicate that the transformer can successfully navigate the complex interactions present in non-Markovian systems when trained with a moderate dataset, showcasing robust generalization via transfer learning.

The implications of employing transformers in quantum control are far-reaching. Practically, the rapid deployment and efficient prediction of control parameters in real-time suggest that such architectures could be seamlessly integrated into quantum technologies, ranging from quantum error correction frameworks to the regulation of quantum devices amid noisy environments. Theoretically, this work invites further exploration into the coupling of advanced AI models with quantum systems, potentially leading to novel insights and methodologies for quantum state management.

Speculating on future developments, the integration of such transformer-based architectures could reframe current quantum computation strategies, fostering innovation especially in scenarios demanding high precision and adaptability to noisy, real-world conditions. Additionally, combining these architectures with other emerging AI techniques could further enhance robustness and efficiency, propelling quantum control systems closer to deployment in more complex, multi-qubit environments.

In conclusion, this paper elucidates the promising avenue of transformer neural networks in quantum feedback control, establishing a new paradigm for leveraging AI in quantum technology applications. This work paves the way for subsequent research to build on these first steps, potentially unlocking unprecedented control capabilities within quantum systems.