QAIS uses a parameterized quantum circuit to allocate Monte Carlo samples along a learned non-separable density and achieves VEGAS-competitive or better accuracy on correlated integrands in simulation.
Analysis of symmetries in the Causal Loop-Tree Duality representations
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abstract
Unveiling hidden symmetries within Feynman diagrams is crucial for achieving more efficient computations in high-energy physics. In this paper, we study the symmetries underlying the causal Loop-Tree Duality (LTD) representations through a {graph-theoretic} analysis. Focusing on the integrand-level representations of $N$-point functions at one loop, we examine their degeneration and discover that different causal representations are interconnected through specific transformations arising from the symmetries of cut diagrams. Furthermore, the degeneration is linked to algebraic constraints among the different causal thresholds. Our findings shed new light on the deeper structures of Feynman integrals and pave the way for significantly accelerating their calculation by interrelating different approaches.
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Unlocking Multidimensional Integration with Quantum Adaptive Importance Sampling
QAIS uses a parameterized quantum circuit to allocate Monte Carlo samples along a learned non-separable density and achieves VEGAS-competitive or better accuracy on correlated integrands in simulation.