Learning finitely correlated states: stability of the spectral reconstruction

TL;DR

This paper presents a method for learning minimal-dimension matrix product operator representations of finitely correlated quantum states with provable error bounds and sample complexity, applicable to infinite and finite chains.

Contribution

It introduces an algorithm with explicit error and sample complexity bounds for reconstructing finitely correlated states from local marginals, including refined bounds for $C^*$-finitely correlated states.

Findings

Bound on trace norm error for the learning algorithm.

Sample complexity of $O(t^2)$ for infinite chains.

Sample complexity of $ ilde{O}(t^3)$ for finite chains.

Abstract

Matrix product operators allow efficient descriptions (or realizations) of states on a 1D lattice. We consider the task of learning a realization of minimal dimension from copies of an unknown state, such that the resulting operator is close to the density matrix in trace norm. For finitely correlated translation-invariant states on an infinite chain, a realization of minimal dimension can be exactly reconstructed via linear algebra operations from the marginals of a size depending on the representation dimension. We establish a bound on the trace norm error for an algorithm that estimates a candidate realization from estimates of these marginals and outputs a matrix product operator, estimating the state of a chain of arbitrary length $t$. This bound allows us to establish an $O(t^2)$ upper bound on the sample complexity of the learning task, with an explicit dependence on the site dimension, realization dimension and spectral properties of a certain map constructed from the state. A refined error bound can be proven for $C^*$-finitely correlated states, which have an operational interpretation in terms of sequential quantum channels applied to the memory system. We can also obtain an analogous error bound for a class of matrix product density operators on a finite chain reconstructible by local marginals. In this case, a linear number of marginals must be estimated, obtaining a sample complexity of $\tilde{O}(t^3)$. The learning algorithm also works for states that are sufficiently close to a finitely correlated state, with the potential of providing competitive algorithms for other interesting families of states.