Optimal entanglement witnesses: a scalable data-driven approach

TL;DR

This paper introduces a scalable, data-driven method for certifying multipartite entanglement by identifying optimal entanglement witnesses through classical field theory mappings and inverse statistical problem solving.

Contribution

It presents a novel approach that efficiently determines entanglement compatibility and constructs optimal witnesses from finite quantum measurement data.

Findings

Effective entanglement certification for quantum data sets.

Polynomial-time solution for non-glassy classical field theories.

Framework applicable to scalable quantum device certification.

Abstract

Multipartite entanglement is the key resource allowing quantum devices to outperform their classical counterparts, and entanglement certification is fundamental to assess any quantum advantage. The only scalable certification scheme relies on entanglement witnessing, typically effective only for special entangled states. Here we focus on finite sets of measurements on quantum states (hereafter called quantum data); and we propose an approach which, given a particular spatial partitioning of the system of interest, can effectively ascertain whether or not the data set is compatible with a separable state. When compatibility is disproved, the approach produces the optimal entanglement witness for the quantum data at hand. Our approach is based on mapping separable states onto equilibrium classical field theories on a lattice; and on mapping the compatibility problem onto an inverse statistical problem, whose solution is reached in polynomial time whenever the classical field theory does not describe a glassy system. Our results pave the way for systematic entanglement certification in quantum devices, optimized with respect to the accessible observables.