Construction of Chemistry Inspired Dynamic Ansatz Utilizing Generative Machine Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912269845135360 |
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| author | Halder, Sonaldeep Anand, Kartikey Maitra, Rahul |
| author_facet | Halder, Sonaldeep Anand, Kartikey Maitra, Rahul |
| contents | Generative machine learning models like the Restricted Boltzmann Machine (RBM) provide a practical approach for ansatz construction within the quantum computing framework. This work introduces a method that efficiently leverages RBM and many-body perturbative measures to build a compact chemistry-inspired ansatz for determining accurate molecular energetics. By training on low-rank determinants derived from an approximate wavefunction, RBM predicts the key high-rank determinants that dominate the ground-state wavefunction. A shallow depth ansatz is constructed to explicitly incorporate these dominant determinants after dynamically decomposing them into low-rank components and applying many-body perturbative measures for further screening. The method requires no additional measurements beyond the initial training phase. Moreover, it incorporates Bayesian hyperparameter optimization for the RBM, ensuring efficient performance with minimal training data during its limited usage. This approach facilitates the efficient computation of molecular properties, paving the way for exploring new chemical phenomena with near-term quantum computers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Construction of Chemistry Inspired Dynamic Ansatz Utilizing Generative Machine Learning Halder, Sonaldeep Anand, Kartikey Maitra, Rahul Chemical Physics Generative machine learning models like the Restricted Boltzmann Machine (RBM) provide a practical approach for ansatz construction within the quantum computing framework. This work introduces a method that efficiently leverages RBM and many-body perturbative measures to build a compact chemistry-inspired ansatz for determining accurate molecular energetics. By training on low-rank determinants derived from an approximate wavefunction, RBM predicts the key high-rank determinants that dominate the ground-state wavefunction. A shallow depth ansatz is constructed to explicitly incorporate these dominant determinants after dynamically decomposing them into low-rank components and applying many-body perturbative measures for further screening. The method requires no additional measurements beyond the initial training phase. Moreover, it incorporates Bayesian hyperparameter optimization for the RBM, ensuring efficient performance with minimal training data during its limited usage. This approach facilitates the efficient computation of molecular properties, paving the way for exploring new chemical phenomena with near-term quantum computers. |
| title | Construction of Chemistry Inspired Dynamic Ansatz Utilizing Generative Machine Learning |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2503.08149 |