The generative quantum eigensolver (GQE) and its application for ground state search
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912616179302400 |
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| author | Nakaji, Kouhei Kristensen, Lasse Bjørn Kemmoku, Ryota Campos-Gonzalez-Angulo, Jorge A. Vakili, Mohammad Ghazi Huang, Haozhe Bagherimehrab, Mohsen Gorgulla, Christoph Wong, FuTe McCaskey, Alex Kim, Jin-Sung Nguyen, Thien Rao, Pooja Gao, Qi Sugawara, Michihiko Yamamoto, Naoki Aspuru-Guzik, Alán |
| author_facet | Nakaji, Kouhei Kristensen, Lasse Bjørn Kemmoku, Ryota Campos-Gonzalez-Angulo, Jorge A. Vakili, Mohammad Ghazi Huang, Haozhe Bagherimehrab, Mohsen Gorgulla, Christoph Wong, FuTe McCaskey, Alex Kim, Jin-Sung Nguyen, Thien Rao, Pooja Gao, Qi Sugawara, Michihiko Yamamoto, Naoki Aspuru-Guzik, Alán |
| contents | We introduce the generative quantum eigensolver (GQE), a new quantum computational framework that operates outside the variational quantum algorithm paradigm by applying classical generative models to quantum simulation. The GQE algorithm optimizes a classical generative model to produce quantum circuits with desired properties. Here, we develop a transformer-based implementation, which we name the generative pre-trained transformer-based (GPT) quantum eigensolver (GPT-QE). We show a proof-of-concept of training and pretraining of GPT-QE applied to electronic structure Hamiltonians, and demonstrate its ability illustrated by surpassing coupled cluster singles and doubles (CCSD) for the strong bond dissociation of the nitrogen molecule and approaching chemical accuracy. We also demonstrate the method on real quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09253 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The generative quantum eigensolver (GQE) and its application for ground state search Nakaji, Kouhei Kristensen, Lasse Bjørn Kemmoku, Ryota Campos-Gonzalez-Angulo, Jorge A. Vakili, Mohammad Ghazi Huang, Haozhe Bagherimehrab, Mohsen Gorgulla, Christoph Wong, FuTe McCaskey, Alex Kim, Jin-Sung Nguyen, Thien Rao, Pooja Gao, Qi Sugawara, Michihiko Yamamoto, Naoki Aspuru-Guzik, Alán Quantum Physics We introduce the generative quantum eigensolver (GQE), a new quantum computational framework that operates outside the variational quantum algorithm paradigm by applying classical generative models to quantum simulation. The GQE algorithm optimizes a classical generative model to produce quantum circuits with desired properties. Here, we develop a transformer-based implementation, which we name the generative pre-trained transformer-based (GPT) quantum eigensolver (GPT-QE). We show a proof-of-concept of training and pretraining of GPT-QE applied to electronic structure Hamiltonians, and demonstrate its ability illustrated by surpassing coupled cluster singles and doubles (CCSD) for the strong bond dissociation of the nitrogen molecule and approaching chemical accuracy. We also demonstrate the method on real quantum hardware. |
| title | The generative quantum eigensolver (GQE) and its application for ground state search |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2401.09253 |