| Presentation | 2024-03-23 Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices Naoki Marumo, Yasutaka Wada, Kazunori Ueda, Keiji Kimura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The more numbers of repeat of Ansatz and the more qubit entangling improve learnability of quantum machine learning by variational quantum algorithm(VQA). On the other hand, Quantum gate computor realizing now is called Noisy Intermediate-Scale Quantum computer (NISQ) devices and they contain noises and they don't correct errors. Therefore, the deeper quantum circuit is, it will be more difficult to output the state as theory. It means that If useing NISQ devices for quantum machine learning by VQA, its learnability and the property noise in NISQ devices will be trade-off. Based on the above, this paper evaluates the difference in learnability for each different circuit in noisy environment from the view of accuracy. As a result, qunatum machine learning by VQA needs entanglement.In addition, linear entanglement that cantains minimum gate doesn't decrease accuracy much, even if the number of repeat in Ansatz increase and make the circuit deeper, and its learnability is the same as noiseless simulation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | NISQ / Quantum machine learning / Varatinal quantum algorithm |
| Paper # | CPSY2023-52,DC2023-118 |
| Date of Issue | 2024-03-14 (CPSY, DC) |
| Conference Information | |
| Committee | DC / CPSY / IPSJ-SLDM / IPSJ-EMB / IPSJ-ARC |
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| Conference Date | 2024/3/21(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Ikinoshima Hall |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | ETNET2024 |
| Chair | Tatsuhiro Tsuchiya(Osaka Univ.) / Kota Nakajima(Fujitsu Lab.) / Hiroyuki Ochi(Ritsumeikan Univ.) / / Tomoaki Tsumura(Nagoya Inst. of Tech.) |
| Vice Chair | Toshinori Hosokawa(Nihon Univ.) / Yasushi Inoguchi(JAIST) / Tomoaki Tsumura(Nagoya Inst. of Tech.) |
| Secretary | Toshinori Hosokawa(Nihon Univ.) / Yasushi Inoguchi(Chiba Univ.) / Tomoaki Tsumura(Univ. of Tsukuba) / (Hitachi) / (Meiji Univ.) / (Toyama Prefectural Univ.) |
| Assistant | / Ryuichi Sakamoto(Tokyo Inst. of Tech.) / Takumi Honda(Fujitsu) |
| Paper Information | |
| Registration To | Technical Committee on Dependable Computing / Technical Committee on Computer Systems / Special Interest Group on System and LSI Design Methodology / Special Interest Group on Embedded Systems / Special Interest Group on System Architecture |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices |
| Sub Title (in English) | |
| Keyword(1) | NISQ |
| Keyword(2) | Quantum machine learning |
| Keyword(3) | Varatinal quantum algorithm |
| 1st Author's Name | Naoki Marumo |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Yasutaka Wada |
| 2nd Author's Affiliation | Meisei University(Meisei Univ.) |
| 3rd Author's Name | Kazunori Ueda |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| 4th Author's Name | Keiji Kimura |
| 4th Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2024-03-23 |
| Paper # | CPSY2023-52,DC2023-118 |
| Volume (vol) | vol.123 |
| Number (no) | CPSY-450,DC-451 |
| Page | pp.pp.82-87(CPSY), pp.82-87(DC), |
| #Pages | 6 |
| Date of Issue | 2024-03-14 (CPSY, DC) |