Quantum Image Classification: Experiments on Utility-Scale Quantum Computers
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908319980978176 |
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| author | Gharibyan, Hrant Karapetyan, Hovnatan Sedrakyan, Tigran Subasic, Pero Su, Vincent P. Tanin, Rudy H. Tepanyan, Hayk |
| author_facet | Gharibyan, Hrant Karapetyan, Hovnatan Sedrakyan, Tigran Subasic, Pero Su, Vincent P. Tanin, Rudy H. Tepanyan, Hayk |
| contents | We perform image classification on the Honda Scenes Dataset on Quantinuum's H-2 and IBM's Heron chips utilizing up to 72 qubits and thousands of two-qubit gates. For data loading, we extend the hierarchical learning to the task of approximate amplitude encoding and block amplitude encoding for commercially relevant images up to 2 million pixels. Hierarchical learning enables the training of variational circuits with shallow enough resources to fit within the classification pipeline. For comparison, we also study how classifier performance is affected by using piecewise angle encoding. At the end of the VQC, we employ a fully-connected layer between measured qubits and the output classes. Some deployed models are able to achieve above 90\% accuracy even on test images. In comparing with classical models, we find we are able to achieve close to state of the art accuracy with relatively few parameters. These results constitute the largest quantum experiment for image classification to date. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Image Classification: Experiments on Utility-Scale Quantum Computers Gharibyan, Hrant Karapetyan, Hovnatan Sedrakyan, Tigran Subasic, Pero Su, Vincent P. Tanin, Rudy H. Tepanyan, Hayk Quantum Physics We perform image classification on the Honda Scenes Dataset on Quantinuum's H-2 and IBM's Heron chips utilizing up to 72 qubits and thousands of two-qubit gates. For data loading, we extend the hierarchical learning to the task of approximate amplitude encoding and block amplitude encoding for commercially relevant images up to 2 million pixels. Hierarchical learning enables the training of variational circuits with shallow enough resources to fit within the classification pipeline. For comparison, we also study how classifier performance is affected by using piecewise angle encoding. At the end of the VQC, we employ a fully-connected layer between measured qubits and the output classes. Some deployed models are able to achieve above 90\% accuracy even on test images. In comparing with classical models, we find we are able to achieve close to state of the art accuracy with relatively few parameters. These results constitute the largest quantum experiment for image classification to date. |
| title | Quantum Image Classification: Experiments on Utility-Scale Quantum Computers |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.10595 |