Artificial intelligence for representing and characterizing quantum systems

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.

32 pages. Comments are welcome

References in corpus (22)

• Scaling Laws for Neural Language Models
• Learning phase transitions by confusion
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Machine learning meets quantum physics
• Quantum circuit optimization with deep reinforcement learning
• Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom Arrays
• Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design
• Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
• Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers
• Quantum State Tomography Inspired by Language Modeling
• When can classical neural networks represent quantum states?
• Mind the gaps: The fraught road to quantum advantage
• Quantum computing and artificial intelligence: status and perspectives
• Quantum circuit complexity and unsupervised machine learning of topological order
• ShadowGPT: Learning to Solve Quantum Many-Body Problems from Randomized Measurements
• On the hardness of learning ground state entanglement of geometrically local Hamiltonians
• Concept learning of parameterized quantum models from limited measurements
• Machine learning the effects of many quantum measurements
• Learning measurement-induced phase transitions using attention
• Generative Decoding for Quantum Error-correcting Codes
• Interpretable Machine Learning in Physics: A Review