Direct measurement of large-scale quantum states

TL;DR

This paper introduces a novel measurement technique using non-Hermitian operators to directly determine large-scale quantum states, significantly improving the efficiency of characterizing high-dimensional entangled states.

Contribution

The authors propose a method employing column operators to directly measure complex quantum state coefficients, enabling large-scale state characterization with enhanced accuracy and efficiency.

Findings

Successfully characterized a 100,000-dimensional entangled state experimentally.

Demonstrated that for purities above 0.81, the dominant eigenvector can be reliably extracted.

Achieved a two-order-of-magnitude improvement over previous methods in large-scale quantum state characterization.

Abstract

In quantum mechanics, predictions are made by way of calculating expectation values of observables, which take the form of Hermitian operators. It is far less common to exploit non-Hermitian operators to perform measurements. Here, we show that the expectation values of a particular set of non-Hermitian matrices, which we call column operators, directly yield the complex coefficients of a quantum state vector. We provide a definition of the state vector in terms of measurable quantities by decomposing the column operators into observables. The technique we propose renders very-large-scale quantum states significantly more accessible in the laboratory, as we demonstrate by experimentally characterising a 100 000-dimensional entangled state. This represents an improvement of two orders of magnitude with respect to previous characterisations of discrete entangled states. Furthermore, in numerical studies, we consider mixed quantum states and show that for purities greater that 0.81, we can reliably extract the most significant eigenvector of the density matrix with a probability greater than 99%. We anticipate that our method will prove to be a useful asset in the quest for understanding and manipulating large-scale quantum systems.