Experimental Quantum Tomography of Multimode Gaussian States

TL;DR

This paper introduces an efficient maximum-likelihood tomography method for multimode Gaussian states that improves accuracy and physicality over traditional techniques, demonstrated through experimental generation and analysis of complex entangled states.

Contribution

The authors develop a covariance-matrix-based tomography approach that ensures physical states and enhances reconstruction accuracy, suitable for large-scale quantum systems.

Findings

The method outperforms conventional approaches in fidelity and physicality.

Experimental generation of multipartite entangled states demonstrates the method's practical applicability.

Reconstructed covariance matrices enable detailed analysis of entanglement and multimode structure.

Abstract

Multimode Gaussian states are a versatile resource for quantum information technologies and have been realized across a wide range of physical platforms. Recent progress in the large-scale generation of such states provides a key ingredient for scalable quantum technologies. Despite the importance of accurately characterizing these states, conventional tomography methods are often impractical because they require large sample sizes and can yield unphysical states. Here we present a reliable and efficient tomography method for multimode Gaussian states based on maximum-likelihood estimation. By directly operating on covariance matrices, the method avoids the exponential overhead associated with density-matrix reconstruction. We consider two commonly used detection schemes--single and joint homodyne detection--and systematically analyze the reconstruction performance. Our method outperforms conventional approaches by ensuring physical covariance matrices and achieving better agreement with the true states. To demonstrate the experimental applicability of the method, we experimentally generate various multipartite entangled states--six-mode graph states with different connectivity, a six-mode GHZ state, and a fully connected ten-mode graph state--and reconstruct their covariance matrices. Using the reconstructed covariance matrices, we quantify fidelities, detect entanglement, and reveal the multimode structure of squeezing and noise. Our technique offers a practical diagnostic tool for developing scalable quantum technologies.