arXiv:2606.15986v1 Announce Type: cross Abstract: The generating functional in quantum field theory provides the natural framework for constructing correlation functions as derivatives with respect to source operators. We present a methodology that leverages machine-learned normalizing flows to reduce the variance of arbitrary $N$-point correlation functions of bosonic operators in lattice gauge field theory calculations by encoding a representation of the generating functional. We show that it is possible to systematically approach noiseless estimators of correlation functions in this framewo
This development arises from ongoing research at the intersection of machine learning and quantum field theory, leveraging advancements in normalizing flows to address computational challenges in areas like lattice QCD.
A strategic reader should care because improving the efficiency and accuracy of quantum field theory calculations, particularly in lattice QCD, can accelerate fundamental physics research and potentially impact related computational fields.
The application of machine-learned normalizing flows introduces a new method for variance reduction in complex physics simulations, potentially making these calculations more tractable and precise.
- · Theoretical physicists
- · High-performance computing researchers
- · AI/ML researchers in scientific computing
- · Quantum Field Theory research institutions
- · Traditional variance reduction methods
- · Computational approaches that struggle with signal-to-noise ratios
Physicists can perform more accurate and computationally feasible simulations of quantum phenomena.
This methodology could be adapted to other computationally intensive scientific simulations requiring variance reduction.
Accelerated fundamental physics discoveries might eventually inform advancements in materials science or other applied fields.
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Read at arXiv cs.LG