Enhancing the ergodicity of Worldvolume HMC via embedding generalized thimble HMC
Masafumi Fukuma, Yusuke Namekawa

TL;DR
This paper introduces an embedding of generalized thimble HMC into Worldvolume HMC to improve ergodicity and efficiency in simulating larger lattice systems, specifically applied to the doped Hubbard model.
Contribution
It proposes a novel combined algorithm that embeds GT-HMC into WV-HMC, enhancing ergodic exploration and enabling larger system simulations with controlled errors.
Findings
The combined algorithm agrees with standalone methods within statistical errors.
It allows simulations on larger lattices, exemplified by an 8x8 doped Hubbard model.
Feasibility is demonstrated through extrapolation to zero Trotter step at fixed temperature.
Abstract
The Worldvolume Hybrid Monte Carlo (WV-HMC) method [arXiv:2012.08468] is an efficient and versatile algorithm that mitigates the sign problem while resolving the ergodicity issues inherent in Lefschetz-thimble approaches. We focus on cases where the maximum flow time can be kept small, such as when applying WV-HMC to the doped Hubbard model utilizing a redundant, nonphysical parameter. An optimal choice of this parameter significantly reduces the sign problem on the original integration surface. This allows for small flow times, thereby enabling the simulation of larger system sizes at a modest computational cost. However, when the worldvolume reduces to a thin layer, phase-space exploration becomes inefficient, and ergodicity problems may reemerge. To address this limitation in WV-HMC, we propose embedding generalized thimble HMC (GT-HMC) into the WV-HMC framework. GT-HMC performs…
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