Computing linear sections of varieties: quantum entanglement, tensor decompositions and beyond

TL;DR

This paper develops an efficient algorithm for finding intersections of linear subspaces with algebraic varieties, including rank-1 matrices, with applications to quantum entanglement and tensor decompositions, overcoming NP-hardness in typical cases.

Contribution

It introduces a polynomial-time algorithm for typical subspaces intersecting with varieties, enabling solutions to problems previously considered NP-hard, such as quantum entanglement and low-rank tensor decompositions.

Findings

Efficient polynomial-time algorithms for entanglement detection in generic subspaces.

New uniqueness and genericity guarantees for low-rank decompositions.

Algorithmic solutions extend beyond tensor decompositions to broader varieties.

Abstract

We study the problem of finding elements in the intersection of an arbitrary conic variety in $\mathbb{F}^n$ with a given linear subspace (where $\mathbb{F}$ can be the real or complex field). This problem captures a rich family of algorithmic problems under different choices of the variety. The special case of the variety consisting of rank-1 matrices already has strong connections to central problems in different areas like quantum information theory and tensor decompositions. This problem is known to be NP-hard in the worst case, even for the variety of rank-1 matrices. Surprisingly, despite these hardness results we develop an algorithm that solves this problem efficiently for "typical" subspaces. Here, the subspace $U \subseteq \mathbb{F}^n$ is chosen generically of a certain dimension, potentially with some generic elements of the variety contained in it. Our main result is a guarantee that our algorithm recovers all the elements of $U$ that lie in the variety, under some mild non-degeneracy assumptions on the variety. As corollaries, we obtain the following new results: $\bullet$ Polynomial time algorithms for several entangled subspaces problems in quantum entanglement, including determining r-entanglement, complete entanglement, and genuine entanglement of a subspace. While all of these problems are NP-hard in the worst case, our algorithm solves them in polynomial time for generic subspaces of dimension up to a constant multiple of the maximum possible. $\bullet$ Uniqueness results and polynomial time algorithmic guarantees for generic instances of a broad class of low-rank decomposition problems that go beyond tensor decompositions. Here, we recover a decomposition of the form $\sum_{i=1}^R v_i \otimes w_i$, where the $v_i$ are elements of the variety $X$. This implies new uniqueness results and genericity guarantees even in the special case of tensor decompositions.