Deterministic Approximation Algorithms for Volumes of Spectrahedra
Mahmut Levent Do\u{g}an, Jonathan Leake, Mohan Ravichandran

TL;DR
This paper introduces a deterministic approximation method for calculating the volumes of spectrahedra using convex optimization, with applications to quantum states and density matrices, inspired by statistical physics principles.
Contribution
The paper develops a novel approximation algorithm for spectrahedra volumes based on the maximum-entropy principle, extending techniques from polytopes to quantum-inspired convex sets.
Findings
Provides asymptotic volume formulas for spectrahedra.
Establishes that central sections of density matrices have equal asymptotic volume.
Applies the method to quantum states with maximal entanglement and density matrix sections.
Abstract
We give a method for computing asymptotic formulas and approximations for the volumes of spectrahedra, based on the maximum-entropy principle from statistical physics. The method gives an approximate volume formula based on a single convex optimization problem of minimizing over the spectrahedron. Spectrahedra can be described as affine slices of the convex cone of positive semi-definite (PSD) matrices, and the method yields efficient deterministic approximation algorithms and asymptotic formulas whenever the number of affine constraints is sufficiently dominated by the dimension of the PSD cone. Our approach is inspired by the work of Barvinok and Hartigan who used an analogous framework for approximately computing volumes of polytopes. Spectrahedra, however, possess a remarkable feature not shared by polytopes, a new fact that we also prove: central sections of the…
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods
