Simple, reliable and noise-resilient continuous-variable quantum state tomography with convex optimization

• URL: http://arxiv.org/abs/2202.11584v2
• Date: Thu, 27 Oct 2022 11:05:10 GMT
• Title: Simple, reliable and noise-resilient continuous-variable quantum state tomography with convex optimization
• Authors: Ingrid Strandberg
• Abstract summary: We present a method for continuous variable state reconstruction based on convex optimization. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.
• Score: 6.127600411727684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise reconstruction of unknown quantum states from measurement data, a process commonly called quantum state tomography, is a crucial component in the development of quantum information processing technologies. Many different tomography methods have been proposed over the years. Maximum likelihood estimation is a prominent example, being the most popular method for a long period of time. Recently, more advanced neural network methods have started to emerge. Here, we go back to basics and present a method for continuous variable state reconstruction that is both conceptually and practically simple, based on convex optimization. Convex optimization has been used for process tomography and qubit state tomography, but seems to have been overlooked for continuous variable quantum state tomography. We demonstrate high-fidelity reconstruction of an underlying state from data corrupted by thermal noise and imperfect detection, for both homodyne and heterodyne measurements. A major advantage over other methods is that convex optimization algorithms are guaranteed to converge to the optimal solution.

Related papers

• Fourier Quantum Process Tomography [0.0]
  We introduce a new technique, referred to as Fourier Quantum Process Tomography, which requires a reduced number of measurements. We experimentally test the technique on different space-dependent polarization transformations, reporting average fidelities higher than 90%.
  arXiv  Detail & Related papers  (2023-12-20T22:17:56Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• 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)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Quantum verification and estimation with few copies [63.669642197519934]
  The verification and estimation of large entangled systems represents one of the main challenges in the employment of such systems for reliable quantum information processing. This review article presents novel techniques focusing on a fixed number of resources (sampling complexity) and thus prove suitable for systems of arbitrary dimension. Specifically, a probabilistic framework requiring at best only a single copy for entanglement detection is reviewed, together with the concept of selective quantum state tomography.
  arXiv  Detail & Related papers  (2021-09-08T18:20:07Z)
• 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)
• Fast and robust quantum state tomography from few basis measurements [65.36803384844723]
  We present an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy. The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory. Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography.
  arXiv  Detail & Related papers  (2020-09-17T11:28:41Z)
• Experimental adaptive quantum state tomography based on rank-preserving transformations [0.0]
  Quantum tomography is a process of quantum state reconstruction using data from multiple measurements. One of the recently proposed methods of quantum tomography is the algorithm based on rank-preserving transformations. We present numerical and experimental comparisons of rank-preserving tomography with another adaptive method.
  arXiv  Detail & Related papers  (2020-08-04T16:49:03Z)
• 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)

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.