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

• URL: http://arxiv.org/abs/2411.03285v1
• Date: Tue, 05 Nov 2024 17:34:03 GMT
• Title: ShadowGPT: Learning to Solve Quantum Many-Body Problems from Randomized Measurements
• Authors: Jian Yao, Yi-Zhuang You,
• Abstract summary: 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.
• Score: 2.1946359779523332
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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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