Experimental Quantum State Tomography of Multimode Gaussian States

• URL: http://arxiv.org/abs/2603.21380v1
• Date: Sun, 22 Mar 2026 19:50:43 GMT
• Title: Experimental Quantum State Tomography of Multimode Gaussian States
• Abstract summary: Multimode Gaussian states are a versatile resource for quantum information technologies.<n>Recent progress in the large-scale generation of such states provides a key ingredient for scalable quantum technologies.<n>Here we present a reliable and efficient tomography method for multimode Gaussian states based on maximum-likelihood estimation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A joint optimization approach to identifying sparse dynamics using least squares kernel collocation [70.13783231186183]
  We develop an all-at-once modeling framework for learning systems of ordinary differential equations (ODE) from scarce, partial, and noisy observations of the states.<n>The proposed methodology amounts to a combination of sparse recovery strategies for the ODE over a function library combined with techniques from reproducing kernel Hilbert space (RKHS) theory for estimating the state and discretizing the ODE.
  arXiv  Detail & Related papers  (2025-11-23T18:04:15Z)
• Efficient Convex Optimization for Bosonic State Tomography [16.426454102413523]
  Quantum states encoded in electromagnetic fields, also known as bosonic states, have been widely applied in quantum sensing, quantum communication, and quantum error correction.<n> Accurate characterization is therefore essential yet difficult when states cannot be reconstructed with sparse Pauli measurements.<n>Tomography must work with dense measurement bases, high-dimensional Hilbert spaces, and often sample-based data.
  arXiv  Detail & Related papers  (2025-09-08T03:12:36Z)
• Energy-independent tomography of Gaussian states [0.29998889086656577]
  We present an efficient and experimentally feasible Gaussian state tomography algorithm with provable recovery trace-distance guarantees.<n>Our algorithm is particularly well-suited for applications in quantum metrology and sensing.
  arXiv  Detail & Related papers  (2025-08-20T18:03:33Z)
• Learning topological states from randomized measurements using variational tensor network tomography [0.4818215922729967]
  Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors.<n>We implement and study a tomographic method that combines variational optimization on tensor networks with randomized measurement techniques.<n>We demonstrate its ability to learn the ground state of the surface code Hamiltonian as well as an experimentally realizable quantum spin liquid state.
  arXiv  Detail & Related papers  (2024-05-31T21:05:43Z)
• Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory [50.24983453990065]
  An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers. We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states. By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
  arXiv  Detail & Related papers  (2024-01-31T15:50:50Z)
• Quantum state tomography with tensor train cross approximation [84.59270977313619]
  We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements.
  arXiv  Detail & Related papers  (2022-07-13T17:56:28Z)
• Efficient Quantum State Tracking in Noisy Environments [10.762101459838052]
  We present an experimental implementation of matrix-exponentiated gradient tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
  arXiv  Detail & Related papers  (2022-05-12T22:32:14Z)
• Fast quantum state reconstruction via accelerated non-convex programming [8.19144665585397]
  We propose a new quantum state method that combines ideas from compressed sensing, non- state noise optimization, and better acceleration methods. We find that the proposed code performs better in both synthetic and real experiments.
  arXiv  Detail & Related papers  (2021-04-14T17:38:40Z)
• Efficient construction of tensor-network representations of many-body Gaussian states [59.94347858883343]
  We present a procedure to construct tensor-network representations of many-body Gaussian states efficiently and with a controllable error. These states include the ground and thermal states of bosonic and fermionic quadratic Hamiltonians, which are essential in the study of quantum many-body systems.
  arXiv  Detail & Related papers  (2020-08-12T11:30:23Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)
• Gaussian Process States: A data-driven representation of quantum many-body physics [59.7232780552418]
  We present a novel, non-parametric form for compactly representing entangled many-body quantum states. The state is found to be highly compact, systematically improvable and efficient to sample. It is also proven to be a universal approximator' for quantum states, able to capture any entangled many-body state with increasing data set size.
  arXiv  Detail & Related papers  (2020-02-27T15:54:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.