| Budget Amount *help |
¥17,420,000 (Direct Cost: ¥13,400,000、Indirect Cost: ¥4,020,000)
Fiscal Year 2023: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2022: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2021: ¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2020: ¥6,760,000 (Direct Cost: ¥5,200,000、Indirect Cost: ¥1,560,000)
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| Outline of Final Research Achievements |
This study focuses on deepening the fundamental principles of the Markov Chain Monte Carlo (MCMC) method and expanding its applications. In particular, the principles of probabilistic potential switching and novel updating methods for the path-integral MCMC were proposed, successfully reducing the autocorrelation time in simulations. Additionally, we calculated the finite-temperature phase transitions of the quantum dimer model and the entanglement entropy of quantum many-body systems. We developed high-speed updating algorithms for MCMC with self-learning and quantum correlations in higher-dimensional systems. Furthermore, we created a tensor network MCMC method, achieving exponential acceleration of the MCMC and finding a path to solving the sign problem.
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