The generative quantum eigensolver (GQE) and its application for ground state search

• URL: http://arxiv.org/abs/2401.09253v1
• Date: Wed, 17 Jan 2024 14:58:19 GMT
• Title: The generative quantum eigensolver (GQE) and its application for ground state search
• Authors: Kouhei Nakaji, Lasse Bj{\o}rn Kristensen, Jorge A. Campos-Gonzalez-Angulo, Mohammad Ghazi Vakili, Haozhe Huang, Mohsen Bagherimehrab, Christoph Gorgulla, FuTe Wong, Alex McCaskey, Jin-Sung Kim, Thien Nguyen, Pooja Rao, Alan Aspuru-Guzik
• Abstract summary: We introduce the generative quantum eigensolver (GQE), a novel method for applying classical generative models for quantum simulation. We demonstrate the effectiveness of training and pre-training GPT-QE in the search for ground states of electronic structure Hamiltonians.
• Score: 6.7844469066080215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the generative quantum eigensolver (GQE), a novel method for applying classical generative models for 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), leveraging both pre-training on existing datasets and training without any prior knowledge. We demonstrate the effectiveness of training and pre-training GPT-QE in the search for ground states of electronic structure Hamiltonians. GQE strategies can extend beyond the problem of Hamiltonian simulation into other application areas of quantum computing.

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