ShadowGPT: Learning to Solve Quantum Many-Body Problems from Randomized Measurements

Understand

We propose ShadowGPT, a novel approach for solving quantum many-body problems by learning from randomized measurement data collected from quantum experiments.

• The model is a generative pretrained transformer (GPT) trained on simulated classical shadow data of ground states of quantum Hamiltonians, obtained through randomized Pauli measurements.
• Once trained, the model can predict a range of ground state properties across the Hamiltonian parameter space.
• We demonstrate its effectiveness on the transverse-field Ising model and the $\mathbb{Z}_2 \times \mathbb{Z}_2$ cluster-Ising model, accurately predicting ground state energy, correlation functions, and entanglement entropy.