Monte-Carlo sampling of energy-constrained quantum superpositions in high-dimensional Hilbert spaces
Frank Hantschel, Boris V. Fine

TL;DR
This paper improves Monte-Carlo sampling efficiency for energy-constrained quantum superpositions in high-dimensional Hilbert spaces by optimizing the shape of enclosing manifolds, achieving significant efficiency gains without losing statistical rigor.
Contribution
It introduces a method to optimize the shape of enclosing manifolds for Monte-Carlo sampling, significantly enhancing efficiency in high-dimensional quantum state spaces.
Findings
Sampling efficiency improved by a factor of five in 14-dimensional spaces
Optimized manifolds provide better sampling without compromising statistical accuracy
Insights into the geometry of energy-constrained quantum manifolds gained
Abstract
Recent studies into the properties of quantum statistical ensembles in high-dimensional Hilbert spaces have encountered difficulties associated with the Monte-Carlo sampling of quantum superpositions constrained by the energy expectation value. A straightforward Monte-Carlo routine would enclose the energy constrained manifold within a larger manifold, which is easy to sample, for example, a hypercube. The efficiency of such a sampling routine decreases exponentially with the increase of the dimension of the Hilbert space, because the volume of the enclosing manifold becomes exponentially larger than the volume of the manifold of interest. The present paper explores the ways to optimise the above routine by varying the shapes of the manifolds enclosing the energy-constrained manifold. The resulting improvement in the sampling efficiency is about a factor of five for a 14-dimensional…
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