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Gradient Echo Quantum Memory in Warm Atomic Vapor
Published on: November 11, 2013
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格子モデルの自己検証変数量子シミュレーション
C Kokail1,2, C Maier1,2, R van Bijnen1,2
1Center for Quantum Physics, and Institute for Experimental Physics, University of Innsbruck, Innsbruck, Austria.
Nature
|May 17, 2019
まとめ
格子モデルの自己検証ハイブリッド量子シミュレーションを紹介します このアプローチは,直接のハミルトン式実現なしに複雑な物理問題を研究するために,古典的量子アルゴリズムを使用します.
科学分野:
- 凝縮物質物理学
- 高エネルギー物理学
- 量子情報科学
背景:
- ハイブリッドの古典量子アルゴリズムは 量子資源を最適化するために利用します
- 変数量子シミュレーションは 複雑な量子システムを研究するための 経路を提供します
- アナログ量子シミュレーションでは,ターゲットハミルトニアンの直接実現が必要です.
研究 の 目的:
- 格子モデルの自己検証ハイブリッド変数量子シミュレーションを実証する.
- これまでの難解な量子モデルの 研究を可能にします
- 量子シミュレーションの検証方法を開発する.
主な方法:
- プログラム可能な量子コプロセッサーを 20 キビットまで使った
- クラシック・量子フィードバック・ループを使った ハイブリッド・バリエーション・アルゴリズム
- 標的のハミルトン式対称性を尊重する絡み合った試行状態を生成する.
- 格子シュヴィンガーモデル, 1D量子力学ゲージ理論に焦点を当てた.
主要な成果:
- ハイブリッド変数量子シミュレーションの実験実証
- 格子シュヴィンガーモデルの基本状態とエネルギーギャップの決定.
- シュヴィンガー・ハミルトン分差の測定は,エネルギーに対するアルゴリズムの誤差の見積もりを提供する.
結論:
- この研究は自己検証量子シミュレーションの 技術を進歩させています
- このアプローチにより,直接的な実験的実現を超えて,多様で複雑な量子モデルを研究することができます.
- アルゴリズムの誤差を定量化することで 検証可能な量子シミュレーションへの一歩を 提供します
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