  We introduce a generative pretained transformer (GPT) designed to learn the
measurement outcomes of a neutral atom array quantum computer. Based on a
vanilla transformer, our encoder-decoder architecture takes as input the
interacting Hamiltonian, and outputs an autoregressive sequence of qubit
measurement probabilities. Its performance is studied in the vicinity of a
quantum phase transition in Rydberg atoms in a square lattice array. We explore
the ability of the architecture to generalize, by producing groundstate
measurements for Hamiltonian parameters not seen in the training set. We focus
on examples of physical observables obtained from inference on three different
models, trained in fixed compute time on a single NVIDIA A100 GPU. These can
act as benchmarks for the scaling of larger RydbergGPT models in the future.
Finally, we provide RydbergGPT open source, to aid in the development of
foundation models based off of a wide variety of quantum computer interactions
and data sets in the future.
