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  • Institute for Quantum Computing

    $900K in seed funding supports new quantum projects

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    $900K in seed funding supports new quantum projects

    Tuesday, May 1, 2018 

     

    Exploring how biological processes use quantum effects and developing new nanowire arrays to detect light at the single photon level are two of 10 projects being funded by more than $900,000 from the Quantum Quest Seed Fund.

    The funding program, which will launch its third cycle in this spring, promotes the development of new ideas and applications for quantum devices.

    Funding recipients Michel Gingras and Zoya Leonenko, both of the Faculty of Science, are leading a project to see if biological processes make direct use of quantum effects or simply depend on the influence of quantum physics on chemical bonding and molecular structure.

    “We are particularly interested to explore whether some fundamental aspects of quantum mechanics, such as isotope effects, coherence or entanglement may be at play in brain tissue and neuronal cells,” said Gingras.

    The researchers hope the project will provide insight into a range of biological mysteries, such as olfaction, magneto-navigation by the European Robin, and the actions of lithium in treating mood disorders.

    Next generation quantum sensors

    Michael Reimer, of the Institute for Quantum Computing and the Department of Electrical and Computer Engineering, received funding to develop a novel single-photon detection technology based on semiconductor nanowire p-n junction arrays that have the potential to significantly enhance the ability to detect light at the single photon level over an unprecedented wavelength range from UV to infrared.

    “We are working to demonstrate high-speed single-photon detection with high efficiency over an unprecedented wavelength range from the visible to infrared (450-1600 nm), with no need for cryogenic cooling,” said Reimer. “By eliminating the need for cooling, these devices are much cheaper and advantageous for sensing applications requiring portability such as in the medical field or launching detectors on nanosatellites where weight and space is at a premium.”

    Potential future applications for the project include quantum computing, quantum cryptography, single-molecule fluorescence spectroscopy, time correlated single photon counting, laser remote sensing (LIDAR), and singlet oxygen luminescence for dose monitoring in cancer treatment.

    Quantum Quest Awardees

    Since June 2017, Quantum Quest Seed Fund has awarded funding for the following projects:

    Cycle 1

    • Carbon nanotube monolayer Josephson junction superconducting qubit
      ​
      Na Young Kim, Electrical and Computer Engineering
    • Two-dimensional quantum materials and heterostructures
      Adam Wei Tsen, Chemistry
    • Next Generation Quantum Sensors
      Michael Reimer, Electrical and Computer Engineering
    • Plasmon control of quantum states in semiconductor nanocrystals
      Pavle Radovanovic, Chemistry
    • Quantum information processing with molecular lattices
      Pierre-Nicholas Roy, Chemistry
    • Qubits and quantum effects in the brain
      Michel Gingras, Physics and Astronomy, Zoya Leonenko, Biology and Physics and Astronomy
    • Fabrication of ultra low noise RF squid amplifiers
      Jan Kycia, Physics and Astronomy

    Cycle 2

    • Cryo-CMOS to control and operate 2D fault-tolerant qubit network
      Lan Wei, Electrical and Computer Engineering
    • Harnessing the promise of quantum materials for future electronic devices
      Young Ki Yoon, Electrical and Computing Engineering
    • Ultrafast dynamical studies of valley-based qubits
      Germán Sciaini, Chemistry

    Three additional projects received funding from the TQT Grand Challenge and Technology Development streams:

    • Development of terahertz polariton lasers
      Professor Zbigniew Wasilewski, Electrical and Computer Engineering
    • Quantum light sources based on deterministic photon subtraction
      Michal Bajcsy, Electrical and Computer Engineering
    • Quantum state tomography with machine learning
      Roger Melko, Physics and Astronomy
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