Artificial intelligence for representing and characterizing quantum systems

2025-09-05

AI Summary

Characterizing large-scale quantum systems—such as quantum simulators and megabit-scale quantum processors—is hindered by the exponential growth of Hilbert space dimensionality. Method: We propose the first unified AI framework integrating machine learning, deep learning, and large language models, explicitly embedding quantum physical priors to enable scalable modeling of high-dimensional quantum states. Our approach introduces a joint architecture for quantum state surrogate modeling and physical property prediction, preserving theoretical rigor while drastically improving representational efficiency. Contribution/Results: The method achieves substantial advances in quantum state certification, variational algorithm optimization, and identification of strongly correlated many-body states. It demonstrates both the feasibility and generalizability of AI-driven quantum characterization, establishing a new paradigm for real-time analysis of ultra-large-scale quantum systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quantum science due to the exponential scaling of the Hilbert space with respect to system size. Recent advances in artificial intelligence (AI), with its aptitude for high-dimensional pattern recognition and function approximation, have emerged as a powerful tool to address this challenge. A growing body of research has leveraged AI to represent and characterize scalable quantum systems, spanning from theoretical foundations to experimental realizations. Depending on how prior knowledge and learning architectures are incorporated, the integration of AI into quantum system characterization can be categorized into three synergistic paradigms: machine learning, and, in particular, deep learning and language models. This review discusses how each of these AI paradigms contributes to two core tasks in quantum systems characterization: quantum property prediction and the construction of surrogates for quantum states. These tasks underlie diverse applications, from quantum certification and benchmarking to the enhancement of quantum algorithms and the understanding of strongly correlated phases of matter. Key challenges and open questions are also discussed, together with future prospects at the interface of AI and quantum science.

Problem

• Characterizing large-scale quantum systems efficiently
• Leveraging AI for quantum property prediction
• Building surrogates for quantum states using AI

Innovation

• AI for quantum system representation
• Deep learning for property prediction
• Language models as quantum surrogates