| Paper Abstract and Keywords |
| Presentation |
2023-12-17 17:30
[Poster Presentation]
Analyze convergence of Quantum-Neural -Networks in the over-parametrized regime
Kaito Tanaka (Keio Univ.), Naoki Yamamoto (KQCC) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Quantum neural networks (QNN) are one of the quantum-classical hybrid algorithms, which can be realized with the current Noisy Intermediate-Scale Quantum computers and may have the advantage of using quantum computers. However, one of the serious problems of QNN is that the loss of training is non-convex, and convergence to the global minimum is not guaranteed. A similar problem can be observed in classical NNs, where it is known that the learning process of classical NNs asymptotically approaches kernel regression in the region where the number of training parameters is excessively large.
In this study, we analyze the convergence of training loss in QNN in the over-parameterized regime, as in the classical case.
As a result, we found analytically that when the loss function and data encoding methods are well chosen, the training loss converges globally to the smallest eigenvalue of the data-dependent Hamiltonian.
We also verify the theory through numerical experiments. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Quantum Neural Networks / QuantNeural Tangent Kernel / Over-parameterized regime / / / / / |
| Reference Info. |
IEICE Tech. Rep. |
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| Conference Information |
| Committee |
QIT |
| Conference Date |
2023-12-17 - 2023-12-19 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
OIST |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Quantum Information |
| Paper Information |
| Registration To |
QIT |
| Conference Code |
2023-12-QIT |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Analyze convergence of Quantum-Neural -Networks in the over-parametrized regime |
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Quantum Neural Networks |
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QuantNeural Tangent Kernel |
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Over-parameterized regime |
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| 1st Author's Name |
Kaito Tanaka |
| 1st Author's Affiliation |
Keio University (Keio Univ.) |
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Naoki Yamamoto |
| 2nd Author's Affiliation |
Keio Quantum Computing Center (KQCC) |
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| Date Time |
2023-12-17 17:30:00 |
| Presentation Time |
120 minutes |
| Registration for |
QIT |
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