Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians

Fuente: arXiv
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Main Authors: Tian, Shu, Hu, Jiaqi, Wu, Rebing, Shi, Yu
Format: Preprint
Published: 2026
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author Tian, Shu
Hu, Jiaqi
Wu, Rebing
Shi, Yu
author_facet Tian, Shu
Hu, Jiaqi
Wu, Rebing
Shi, Yu
contents We propose a training-free quantum generative paradigm, which is fundamentally different from current generative models, which demand substantial computational power, face practical scalability limits, and often function as opaque black boxes, despite their remarkable success. We enable image and text generation without parameter training, by constructing a local parent Hamiltonian whose ground state encodes the target distribution and then solving the global Hamiltonian. Rooted directly in quantum mechanical principles, this approach establishes a new pathway for generative modeling that leverages superposition and entanglement to maintain global consistency.
format Preprint
id arxiv_https___arxiv_org_abs_2605_25986
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians
Tian, Shu
Hu, Jiaqi
Wu, Rebing
Shi, Yu
Quantum Physics
We propose a training-free quantum generative paradigm, which is fundamentally different from current generative models, which demand substantial computational power, face practical scalability limits, and often function as opaque black boxes, despite their remarkable success. We enable image and text generation without parameter training, by constructing a local parent Hamiltonian whose ground state encodes the target distribution and then solving the global Hamiltonian. Rooted directly in quantum mechanical principles, this approach establishes a new pathway for generative modeling that leverages superposition and entanglement to maintain global consistency.
title Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians
topic Quantum Physics
url https://arxiv.org/abs/2605.25986