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Updated: Feb 24, 2026

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
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第二量子化における選択されたニューラルネットワーク量子状態の表現力
Zhendong Li1, Tong Zhao2, Bohan Zhang1
1Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
The Journal of chemical physics
|February 23, 2026
まとめ
ニューロン積状態(NPS)は、複雑なフェルミオン量子状態を表現するための新しい方法を提供する。単純な非局所相関子から構築されたこれらの状態は、任意の波動関数を近似でき、量子多体系問題の解決を進歩させる。
科学分野:
- 計算物理学
- 量子力学
- 機械学習
背景:
- ニューラルネットワーク量子状態は、量子多体系問題のための新興ツールである。
- 特にフェルミオンに対するそれらの能力と限界を理解することが重要である。
研究 の 目的:
- フェルミオン状態に対する制限付きボルツマンマシンアンザッツの一般化。
- 量子多体系問題のためのニューロン積状態(NPS)の導入と分析。
主な方法:
- 制限付きボルツマンマシンアンザッツからニューロン積状態(NPS)への一般化。
- 第二量子化におけるNPS、フィードフォワードニューラルネットワーク、およびニューラルネットワークバックフローの普遍的近似能力の証明。
主要な成果:
- NPSは、相関子積状態とは異なる、単純な非局所相関子を利用する。
- NPSは、穏やかな条件下で任意の波動関数を近似できる。
- 第二量子化におけるニューラルネットワーク表現の普遍的近似が確立された。
結論:
- NPSは、フェルミオン量子状態を表現するための新規かつ強力なフレームワークを提供する。
- この研究は、量子多体系問題の解決におけるニューラルネットワークの能力の理解を深める。
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