QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910575686057984 |
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| author | Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Bach, Bao Safro, Ilya |
| author_facet | Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Bach, Bao Safro, Ilya |
| contents | In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed on real quantum hardware. The efficiency of quantum circuit is desired both in the number of trainable gates and the depth of the overall circuit. The major concern of barren plateaus has made this need for efficiency even more acute. The problem of efficient quantum circuit realization has been extensively studied in the literature to reduce gate complexity and circuit depth. Another important approach is to design a method to reduce the \emph{parameter complexity} in a variational quantum circuit. Existing methods include hyperparameter-based parameter pruning which introduces an additional challenge of finding the best hyperparameters for different applications. In this paper, we present \emph{QAdaPrune} - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.\\ \noindent{\bf Reproducibility}: The source code and data are available at \url{https://github.com/aicaffeinelife/QAdaPrune.git} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13352 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Bach, Bao Safro, Ilya Quantum Physics Machine Learning In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed on real quantum hardware. The efficiency of quantum circuit is desired both in the number of trainable gates and the depth of the overall circuit. The major concern of barren plateaus has made this need for efficiency even more acute. The problem of efficient quantum circuit realization has been extensively studied in the literature to reduce gate complexity and circuit depth. Another important approach is to design a method to reduce the \emph{parameter complexity} in a variational quantum circuit. Existing methods include hyperparameter-based parameter pruning which introduces an additional challenge of finding the best hyperparameters for different applications. In this paper, we present \emph{QAdaPrune} - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.\\ \noindent{\bf Reproducibility}: The source code and data are available at \url{https://github.com/aicaffeinelife/QAdaPrune.git} |
| title | QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2408.13352 |