Estimating the properties of quantum systems such as quantum phase has been critical in addressing the essential quantum many-body problems in physics and chemistry. Deep learning models have been recently introduced to property estimation, surpassing  conventional statistical approaches. However, these methods are tailored to the specific task and quantum data at hand. It remains an open and attractive question for devising a more universal task-agnostic pre-training model for quantum property estimation. In this paper, we propose LLM4QPE, a large language model style quantum task-agnostic pre-training and finetuning paradigm that 1) performs unsupervised pretraining on diverse quantum systems with different physical conditions; 2) uses the pretrained model for supervised finetuning and delivers high performance with limited training data, on downstream tasks. It mitigates the cost for quantum data collection and speeds up convergence. Extensive experiments show the promising efficacy of LLM4QPE in various tasks including classifying quantum phases of matter on Rydberg atom model and predicting two-body correlation function on anisotropic Heisenberg model.
Towards LLM4QPE: Unsupervised Pretraining of Quantum Property Estimation and A Benchmark
Yehui Tang Hao Xiong Nianzu Yang Tailong Xiao Junchi Yan