| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-09 |
| タイトル |
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タイトル |
Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning |
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言語 |
ja |
| 言語 |
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言語 |
jpn |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Gekko Budiutama
Shunsuke Daimon
Hirofumi Nishi
Ryui Kaneko
Tomi Ohtsuki
Yu-ichiro Matsushita
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits require a large number of trainable parameters that grows with both qubit count and circuit depth, often rendering training infeasible. In this study, we introduce the Adaptive Interpolating Quantum Transform (AIQT), a quantum-native framework for flexible and efficient learning. AIQT defines a trainable unitary that interpolates between quantum transforms, such as the Hadamard and quantum Fourier transforms. This approach enables expressive quantum state manipulation while controlling parameter overhead. When built upon efficient quantum circuits such as the quantum Fourier transform, AIQT achieves polynomial scaling of the number of gates with respect to the number of qubits. Our results show that AIQT achieves high performance with minimal parameter count, offering a scalable and interpretable alternative to deep variational circuits. |
| 書誌情報 |
Physical Review A
巻 112,
p. 062410,
発行日 2025-11
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| 出版者 |
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出版者 |
The American Physical Society (APS) |
| DOI |
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識別子タイプ |
DOI |
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関連識別子 |
10.1103/vyr3-h9hq |
