| Paper Abstract and Keywords |
| Presentation |
2023-05-30 10:00
A new initial distribution for qGAN to load probability distributions
Yuichi Sano, Ryosuke Koga (Kyoto Univ.), Masaya Abe, Kei Nakagawa (Nomura Asset Management) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and one example is the quantum expectation estimation algorithm that accelerates the widely-used Monte Carlo method in fields such as finance.
A previous study has shown that qGAN, a quantum circuit version of GAN, can generate the probability distribution necessary for the quantum expectation estimation algorithm in shallow quantum circuits.
However, a previous study has also suggested that the convergence speed and accuracy of the generated distribution can vary greatly depending on the initial distribution of the generator of qGAN.
In particular, the effectiveness of using a normal distribution as the initial distribution has been claimed, but it requires a deep quantum circuit, which may lose the advantage of qGAN.
Therefore, in this study, we propose a novel method for generating an initial distribution that improves the efficiency of qGAN learning.
Our method uses the classical process of "label replacement" to generate various probability distributions in shallow quantum circuits similar to the uniform distribution.
We demonstrate that our proposed method can generate the log-normal distribution, which is important in financial engineering, more efficiently than existing methods.
Additionally, we show that the initial distribution proposed in our research is related to the problem of determining the initial weights for qGAN. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Quantum computation / Quantum algorithms / Monte Carlo method / Quantum machine learning / Quantum GAN / qGAN / / |
| Reference Info. |
IEICE Tech. Rep. |
| Paper # |
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| Date of Issue |
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| Download PDF |
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| Conference Information |
| Committee |
QIT |
| Conference Date |
2023-05-29 - 2023-05-30 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Katsura Campus, Kyoto University |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Quantum Information |
| Paper Information |
| Registration To |
QIT |
| Conference Code |
2023-05-QIT |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
A new initial distribution for qGAN to load probability distributions |
| Sub Title (in English) |
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| Keyword(1) |
Quantum computation |
| Keyword(2) |
Quantum algorithms |
| Keyword(3) |
Monte Carlo method |
| Keyword(4) |
Quantum machine learning |
| Keyword(5) |
Quantum GAN |
| Keyword(6) |
qGAN |
| Keyword(7) |
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| Keyword(8) |
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| 1st Author's Name |
Yuichi Sano |
| 1st Author's Affiliation |
Kyoto University (Kyoto Univ.) |
| 2nd Author's Name |
Ryosuke Koga |
| 2nd Author's Affiliation |
Kyoto University (Kyoto Univ.) |
| 3rd Author's Name |
Masaya Abe |
| 3rd Author's Affiliation |
Nomura Asset Management Co,Ltd. (Nomura Asset Management) |
| 4th Author's Name |
Kei Nakagawa |
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Nomura Asset Management Co,Ltd. (Nomura Asset Management) |
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| Speaker |
Author-1 |
| Date Time |
2023-05-30 10:00:00 |
| Presentation Time |
15 minutes |
| Registration for |
QIT |
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| Volume (vol) |
vol. |
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