| Item type |
SIG Technical Reports(1) |
| 公開日 |
2024-10-21 |
| タイトル |
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タイトル |
Performance Analysis of Sample-Based Quantum Diagonalization |
| タイトル |
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言語 |
en |
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タイトル |
Performance Analysis of Sample-Based Quantum Diagonalization |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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IBM Quantum, IBM Research-Tokyo／Department of Nuclear Engineering and Management, Graduate School of Engineering, The University of Tokyo |
| 著者所属 |
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IBM Quantum, IBM Research-Tokyo |
| 著者所属 |
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IBM Quantum, IBM Research-Tokyo |
| 著者所属 |
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IBM Quantum, IBM Research-Tokyo |
| 著者所属(英) |
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en |
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IBM Quantum, IBM Research-Tokyo / Department of Nuclear Engineering and Management, Graduate School of Engineering, The University of Tokyo |
| 著者所属(英) |
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en |
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IBM Quantum, IBM Research-Tokyo |
| 著者所属(英) |
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en |
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IBM Quantum, IBM Research-Tokyo |
| 著者所属(英) |
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en |
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IBM Quantum, IBM Research-Tokyo |
| 著者名 |
Shigetora, Miyashita
Yukio, Kawashima
Hitomi, Takahashi
Hiroshi, Horii
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| 著者名(英) |
Shigetora, Miyashita
Yukio, Kawashima
Hitomi, Takahashi
Hiroshi, Horii
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
We present a novel workflow for sample-based quantum diagonalization (SQD) that combines classical optimization and quantum-selected configuration interaction (QSCI) with configuration recovery. Although quantum computers hold promise in simulating many-body quantum systems, most algorithms, including the variational quantum eigensolver (VQE), are limited by circuit depth and high error rates. To address these challenges, we introduce an implementation of SQD with constrained optimization by linear approximation (COBYLA) optimizer. This paper discusses the runtime of classical resources for performing SQD, its optimization with COBYLA for a small molecule, and the computational time required to simulate larger molecules. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
|
内容記述 |
We present a novel workflow for sample-based quantum diagonalization (SQD) that combines classical optimization and quantum-selected configuration interaction (QSCI) with configuration recovery. Although quantum computers hold promise in simulating many-body quantum systems, most algorithms, including the variational quantum eigensolver (VQE), are limited by circuit depth and high error rates. To address these challenges, we introduce an implementation of SQD with constrained optimization by linear approximation (COBYLA) optimizer. This paper discusses the runtime of classical resources for performing SQD, its optimization with COBYLA for a small molecule, and the computational time required to simulate larger molecules. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA12894105 |
| 書誌情報 |
研究報告量子ソフトウェア（QS）
巻 2024-QS-13,
号 9,
p. 1-7,
発行日 2024-10-21
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2435-6492 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-QS24013009.pdf (1.3 MB)
