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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/2406.08591 |
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| _version_ | 1866914951526875136 |
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| author | Ardila-García, Juan E. Vargas-Calderón, Vladimir González, Fabio A. Useche, Diego H. Vinck-Posada, Herbert |
| author_facet | Ardila-García, Juan E. Vargas-Calderón, Vladimir González, Fabio A. Useche, Diego H. Vinck-Posada, Herbert |
| contents | This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08591 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MEMO-QCD: Quantum Density Estimation through Memetic Optimisation for Quantum Circuit Design Ardila-García, Juan E. Vargas-Calderón, Vladimir González, Fabio A. Useche, Diego H. Vinck-Posada, Herbert Quantum Physics Machine Learning This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware. |
| title | MEMO-QCD: Quantum Density Estimation through Memetic Optimisation for Quantum Circuit Design |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2406.08591 |