Study of quantum many-body systems with frustration and randomness using tensor-network algorithm and quantum information
| Project/Area Number |
15K05198
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | Gunma University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | フラストレート量子スピン系 / ランダム量子スピン系 / 実空間繰り込み群法 / テンソルネットワーク / 密度行列繰り込み群法 / スピン・ネマティック状態 |
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| Outline of Final Research Achievements |
Low-dimensional quantum many-body systems with frustration and randomness were studied. We investigated frustrated quantum spin systems in various two-dimensional lattices and two-leg ladder lattice, and showed that several novel quantum states including the spin-nematic and vector-chirality states emerged in those systems. We also proposed an improvement of the tensor-network strong-disorder renormalization group (tSDRG) method. Applying the improved tSDRG algorithm to quantum spin systems with quenched randomness in one- and two-dimensional lattices, we showed that the algorithm was able to achieve an accurate calculation of various quantities in the ground state of those systems.
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| Academic Significance and Societal Importance of the Research Achievements |
スピンネマティック（SN）状態やカイラル秩序状態は、多スピン秩序変数で記述される新しいタイプの量子秩序状態として注目されている。本研究の結果は、それらの新奇量子秩序状態の理解を促進するものである。特に、通常の量子磁性体がもつ相互作用のみを含む系でSN状態が実現されうるという結果は、実在の物質におけるSN状態の実現可能性を拡げるものとしての意義をもつ。また、二次元フラストレート・ランダム量子多体系は、主に手法上の困難からその特性解明が妨げられていた。本研究で成されたテンソルネットワーク実空間繰り込み群法の改良は、その困難の克服における大きな進歩であり、今後の研究を進展させるものと期待される。
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Report
(6 results)
Research Products
(45 results)
