Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (1/1 displayed)

  • 2023Scalable Quantum Signal Processing with Integrated Photonics and Fiber-based Modulescitations

Places of action

Chart of shared publication
Morandotti, Roberto
1 / 2 shared
Loranger, Sebastien
1 / 1 shared
Caspani, Lucia
1 / 4 shared
Little, Brent E.
1 / 1 shared
Cino, Alfonso
1 / 1 shared
Moss, David J.
1 / 15 shared
Roztocki, Piotr
1 / 1 shared
Montaut, Nicola
1 / 1 shared
Yu, Hao
1 / 4 shared
Reimer, Christian
1 / 1 shared
Kues, Michael
1 / 1 shared
Fischer, Bennet
1 / 1 shared
Maclellan, Benjamin
1 / 1 shared
Wetzel, Benjamin
1 / 2 shared
Chemnitz, Mario
1 / 1 shared
Sciara, Stefania
1 / 1 shared
Kashyap, Raman
1 / 4 shared
Munro, William J.
1 / 1 shared
Cortes, Luis Romero
1 / 1 shared
Azaña, José
1 / 1 shared
Jestin, Yoann
1 / 13 shared
Chart of publication period
2023

Co-Authors (by relevance)

  • Morandotti, Roberto
  • Loranger, Sebastien
  • Caspani, Lucia
  • Little, Brent E.
  • Cino, Alfonso
  • Moss, David J.
  • Roztocki, Piotr
  • Montaut, Nicola
  • Yu, Hao
  • Reimer, Christian
  • Kues, Michael
  • Fischer, Bennet
  • Maclellan, Benjamin
  • Wetzel, Benjamin
  • Chemnitz, Mario
  • Sciara, Stefania
  • Kashyap, Raman
  • Munro, William J.
  • Cortes, Luis Romero
  • Azaña, José
  • Jestin, Yoann
OrganizationsLocationPeople

document

Scalable Quantum Signal Processing with Integrated Photonics and Fiber-based Modules

  • Morandotti, Roberto
  • Loranger, Sebastien
  • Caspani, Lucia
  • Zhang, Yanbing
  • Little, Brent E.
  • Cino, Alfonso
  • Moss, David J.
  • Roztocki, Piotr
  • Montaut, Nicola
  • Yu, Hao
  • Reimer, Christian
  • Kues, Michael
  • Fischer, Bennet
  • Maclellan, Benjamin
  • Wetzel, Benjamin
  • Chemnitz, Mario
  • Sciara, Stefania
  • Kashyap, Raman
  • Munro, William J.
  • Cortes, Luis Romero
  • Azaña, José
  • Jestin, Yoann
Abstract

Quantum photonic resources are critical for advanced applications such as quantum computation, communication, and information processing. Efficient generation and detection of quantum states, as well as reliable photon manipulation techniques, are essential for the development of practical quantum technologies. Integrated photonic platforms offer attractive solutions due to their stability, small device footprint, and improved power efficiencies. However, optical loss and environmental noise hinder their capability to transmit, measure, and detect quantum states with high accuracies. To tackle these limitations, we have developed robust solutions for quantum signal processing by leveraging infrastructures from telecommunications and integrated photonics. These approaches focus on the use of silicon-based photonic sources for entanglement generation in the time and frequency degrees of freedom, as well as chip- and fiber-based architectures for entanglement verification via quantum interference and tomography measurements. Our photonic schemes allow for high-dimensional entanglement processing, demonstrating their versatility in developing scalable and cost-efficient quantum signal processing platforms.© 2023 IEEE.

Topics
  • tomography
  • Silicon