Quantum Deep Learning Still Needs a Quantum Leap
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918183392247808 |
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| author | Gundlach, Hans Kukina, Hrvoje Lynch, Jayson Thompson, Neil |
| author_facet | Gundlach, Hans Kukina, Hrvoje Lynch, Jayson Thompson, Neil |
| contents | Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Deep Learning Still Needs a Quantum Leap Gundlach, Hans Kukina, Hrvoje Lynch, Jayson Thompson, Neil Quantum Physics Artificial Intelligence Machine Learning 81P68, 68T07, 68Q12, 68Q25, 65F10 F.1.2; F.2.1; I.2.6; I.2.8; G.1.3 Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field. |
| title | Quantum Deep Learning Still Needs a Quantum Leap |
| topic | Quantum Physics Artificial Intelligence Machine Learning 81P68, 68T07, 68Q12, 68Q25, 65F10 F.1.2; F.2.1; I.2.6; I.2.8; G.1.3 |
| url | https://arxiv.org/abs/2511.01253 |