Learning the closest product state

TL;DR

This paper presents an algorithm to find a product state close to an unknown quantum state with high fidelity, and proves that estimating this fidelity is NP-hard, revealing fundamental limits in quantum state approximation.

Contribution

The paper introduces an efficient algorithm for approximating the closest product state and establishes NP-hardness of fidelity estimation, connecting quantum learning with computational complexity.

Findings

Algorithm achieves ε-close fidelity with polynomial copies

Estimating optimal fidelity is NP-hard for certain errors

Develops efficient methods in special cases

Abstract

We study the problem of finding a (pure) product state with optimal fidelity to an unknown $n$-qubit quantum state $\rho$, given copies of $\rho$. This is a basic instance of a fundamental question in quantum learning: is it possible to efficiently learn a simple approximation to an arbitrary state? We give an algorithm which finds a product state with fidelity $\varepsilon$-close to optimal, using $N = n^{\text{poly}(1/\varepsilon)}$ copies of $\rho$ and $\text{poly}(N)$ classical overhead. We further show that estimating the optimal fidelity is NP-hard for error $\varepsilon = 1/\text{poly}(n)$, showing that the error dependence cannot be significantly improved. For our algorithm, we build a carefully-defined cover over candidate product states, qubit by qubit, and then demonstrate that extending the cover can be reduced to approximate constrained polynomial optimization. For our proof of hardness, we give a formal reduction from polynomial optimization to finding the closest product state. Together, these results demonstrate a fundamental connection between these two seemingly unrelated questions. Building on our general approach, we also develop more efficient algorithms in three simpler settings: when the optimal fidelity exceeds $5/6$; when we restrict ourselves to a discrete class of product states; and when we are allowed to output a matrix product state.