随机相似重整化群
Stochastic Similarity Renormalization Group
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中文总结 AI 辅助
研究将量子蒙特卡罗技术融入相似重整化群，开发随机SRGQMC框架，能进行自由空间两体和介质中多体演化，规避张量空间爆炸，通过随机技术实现首次IMSRG(4)计算，为高阶计算提供实用途径。
中文摘要 AI 辅助
通过将量子蒙特卡罗技术集成到相似重整化群（SRG）中，我们开发了一个随机SRG框架（SRGQMC），它能够进行自由空间两体和介质中多体演化。该方法通过将连续酉变换映射到有符号随机游走的集合上，规避了多体流方程的组合张量空间爆炸问题。我们将SRGQMC与现实核子 - 核子（NN）相互作用的确定性自由空间SRG演化以及在两体和三体水平上使用理查森配对模型的介质中SRG（IMSRG）多体计算进行了基准测试。虽然由于计算成本过高，确定性扩展到四体水平[IMSRG(4)]仍然不可行，但我们通过使用随机技术实现了首次IMSRG(4)计算，向完全组态相互作用极限展示了显著改进。这个随机框架为高阶IMSRG计算提供了一条实用途径。
英文摘要
By integrating the quantum Monte Carlo technique into the similarity renormalization group (SRG), we have developed a stochastic SRG framework (SRGQMC) capable of both free-space two-body and in-medium many-body evolutions. This approach circumvents the combinatorial tensor-space explosion of many-body flow equations by mapping continuous unitary transformations onto an ensemble of signed random walkers. We benchmark the SRGQMC against deterministic free-space SRG evolutions of realistic nucleon-nucleon (NN) interactions, as well as against in-medium SRG (IMSRG) many-body calculations with the Richardson pairing model at two- and three-body levels [IMSRG(2)/(3)]. While a deterministic extension to the four-body level [IMSRG(4)] remains unfeasible due to prohibitive computational costs, we have achieved the first IMSRG(4) calculation by using the stochastic technique, demonstrating a substantial improvement toward the full configuration-interaction limit. This stochastic framework provides a practical pathway to higher-order IMSRG calculations.
