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

2024/11/05 by Jian Yao, Yi-Zhuang You, Yao, Jian +1 · 3 citations
Computer Science · Physics and Astronomy · #Cold Atom Physics and Bose-Einstein Condensates #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2411.03285

openalex publication_date 2024/11/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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 ℤ₂ × ℤ₂ 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.

Cited by

• Unsupervised Detection of Topological Phase Transitions with a Quantum Reservoir
• Learning quantum many-body data locally: A provably scalable framework
• Artificial intelligence for representing and characterizing quantum systems

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