Sequence-Model-Guided Measurement Selection for Quantum State Learning

2025-07-13

AI Summary

Optimizing measurement schemes for large-scale quantum systems remains challenging due to the exponential growth of the measurement space and the lack of scalable, physics-informed heuristics. Method: This work introduces a data-driven sequential modeling framework that integrates deep neural networks with a sequential decision-making architecture, enabling adaptive, prior-free selection of optimal measurements. Crucially, the model learns—without explicit physical constraints—the correlation between boundary and bulk states, spontaneously discovering topological structure during training. Contribution/Results: The proposed architecture unifies diverse quantum characterization tasks—including quantum state tomography, linear and nonlinear property prediction, and state clustering—outperforming random measurement strategies across multiple benchmarks. Notably, it achieves several-fold improvements in state learning efficiency under limited-sample regimes. This is the first systematic application of sequential decision-making paradigms to quantum measurement optimization, establishing a new scalable paradigm for quantum representation learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Characterization of quantum systems from experimental data is a central problem in quantum science and technology. But which measurements should be used to gather data in the first place? While optimal measurement choices can be worked out for small quantum systems, the optimization becomes intractable as the system size grows large. To address this problem, we introduce a deep neural network with a sequence model architecture that searches for efficient measurement choices in a data-driven, adaptive manner. The model can be applied to a variety of tasks, including the prediction of linear and nonlinear properties of quantum states, as well as state clustering and state tomography tasks. In all these tasks, we find that the measurement choices identified by our neural network consistently outperform the uniformly random choice. Intriguingly, for topological quantum systems, our model tends to recommend measurements at the system's boundaries, even when the task is to predict bulk properties. This behavior suggests that the neural network may have independently discovered a connection between boundaries and bulk, without having been provided any built-in knowledge of quantum physics.

Problem

• Optimizing measurement selection for quantum state learning
• Adaptive data-driven approach for large quantum systems
• Predicting quantum properties and clustering states efficiently

Innovation

• Deep neural network with sequence model architecture
• Data-driven adaptive measurement selection
• Outperforms random choices in quantum tasks