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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.13587 |
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| _version_ | 1866915990065905664 |
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| author | Bensoussan, Avner Chachkarova, Elena Even-Mendoza, Karine Fortz, Sophie Lenihan, Connor |
| author_facet | Bensoussan, Avner Chachkarova, Elena Even-Mendoza, Karine Fortz, Sophie Lenihan, Connor |
| contents | In this paper, we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost's Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver's hyperparameters. These models predict hyperparameter values, leading to a 0.12% reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Quantum Eigensolver Algorithms With Machine Learning Bensoussan, Avner Chachkarova, Elena Even-Mendoza, Karine Fortz, Sophie Lenihan, Connor Quantum Physics Software Engineering In this paper, we explore accelerating Hamiltonian ground state energy calculation on NISQ devices. We suggest using search-based methods together with machine learning to accelerate quantum algorithms, exemplified in the Quantum Eigensolver use case. We trained two small models on classically mined data from systems with up to 16 qubits, using XGBoost's Python regressor. We evaluated our preliminary approach on 20-, 24- and 28-qubit systems by optimising the Eigensolver's hyperparameters. These models predict hyperparameter values, leading to a 0.12% reduction in error when tested on 28-qubit systems. However, due to inconclusive results with 20- and 24-qubit systems, we suggest further examination of the training data based on Hamiltonian characteristics. In future work, we plan to train machine learning models to optimise other aspects or subroutines of quantum algorithm execution beyond its hyperparameters. |
| title | Accelerating Quantum Eigensolver Algorithms With Machine Learning |
| topic | Quantum Physics Software Engineering |
| url | https://arxiv.org/abs/2409.13587 |