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arXiv · 2411.03285

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

Jian Yao·Yi-Zhuang You
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Abstract

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. This approach highlights the potential of combining quantum data with classical machine learning to address complex quantum many-body challenges.

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Jian Yao, Yi-Zhuang You. 2024-11-05. ShadowGPT: Learning to Solve Quantum Many-Body Problems from Randomized Measurements. https://arxiv.org/abs/2411.03285

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