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Analysis of institutional authors

Sánchez Muñoz CAuthor

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October 18, 2021
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Article

Classification and reconstruction of optical quantum states with deep neural networks

Publicated to:PHYSICAL REVIEW RESEARCH. 3 (3): 033278- - 2021-09-27 3(3), DOI: 10.1103/PhysRevResearch.3.033278

Authors: Ahmed, Shahnawaz; Sanchez Munoz, Carlos; Nori, Franco; Kockum, Anton Frisk

Affiliations

Chalmers Univ Technol, Dept Microtechnol & Nanosci, S-41296 Gothenburg, Sweden - Author
Chalmers University of Technology - Author
IFIMAC-Condensed Matter Physics Center - Author
RIKEN, Cluster Pioneering Res, Theoret Quantum Phys Lab, Wako, Saitama 3510198, Japan - Author
RIKEN, Ctr Quantum Comp RQC, Wako, Saitama 3510198, Japan - Author
Univ Autonoma Madrid, Condensed Matter Phys Ctr IFIMAC, Madrid 28049, Spain - Author
Univ Autonoma Madrid, Dept Fis Teor Mat Condensada, Madrid 28049, Spain - Author
Univ Michigan, Dept Phys, Ann Arbor, MI 48109 USA - Author
University of Michigan, Ann Arbor , Riken - Author
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Abstract

We apply deep-neural-network-based techniques to quantum state classification and reconstruction. Our methods demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed QST technique with conditional generative adversarial networks (QST-CGAN) [Ahmed et al., Phys. Rev. Lett.127, 140502 (2021)10.1103/PhysRevLett.127.140502]. We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than iterative and accelerated projected-gradient-based maximum-likelihood estimation (MLE) methods. We also demonstrate that the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than these MLE methods. Our paper opens possibilities to use state-of-the-art deep-learning methods for quantum state classification and reconstruction under various types of noise.

Keywords

dynamicspython frameworkqutiptomographyLearning algorithm

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal PHYSICAL REVIEW RESEARCH, Q4 Agency Scopus (SJR), its regional focus and specialization in Physics and Astronomy (Miscellaneous), give it significant recognition in a specific niche of scientific knowledge at an international level.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 3.86. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 5.49 (source consulted: FECYT Feb 2024)
  • Field Citation Ratio (FCR) from Dimensions: 20.08 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-18, the following number of citations:

  • WoS: 32
  • Scopus: 45

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-18:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 48.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 52 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 21.6.
  • The number of mentions on the social network X (formerly Twitter): 37 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://repositorio.uam.es/handle/10486/702285

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Japan; Sweden; United States of America.