Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914754047508480 |
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| author | Stenzel, Gerhard Zielinski, Sebastian Kölle, Michael Altmann, Philipp Nüßlein, Jonas Gabor, Thomas |
| author_facet | Stenzel, Gerhard Zielinski, Sebastian Kölle, Michael Altmann, Philipp Nüßlein, Jonas Gabor, Thomas |
| contents | To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub https://github.com/gstenzel/qandle and PyPI https://pypi.org/project/qandle/ . |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting Stenzel, Gerhard Zielinski, Sebastian Kölle, Michael Altmann, Philipp Nüßlein, Jonas Gabor, Thomas Quantum Physics Machine Learning To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit execution. Quantum gate matrix caching reduces the overhead of repeated applications of the Kronecker product when applying a gate matrix to the state vector by storing decomposed partial matrices for each gate. Circuit splitting divides the circuit into sub-circuits with fewer gates by constructing a dependency graph, enabling parallel or sequential execution on disjoint subsets of the state vector. These techniques are implemented using the PyTorch machine learning framework. We demonstrate the performance of our approach by comparing it to other PyTorch-compatible quantum state-vector simulators. Our implementation, named Qandle, is designed to seamlessly integrate with existing machine learning workflows, providing a user-friendly API and compatibility with the OpenQASM format. Qandle is an open-source project hosted on GitHub https://github.com/gstenzel/qandle and PyPI https://pypi.org/project/qandle/ . |
| title | Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2404.09213 |