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Universität Innsbruck

Country: Austria
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253 Projects, page 1 of 51
  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 101039522
    Overall Budget: 1,499,790 EURFunder Contribution: 1,499,790 EUR
    Partners: Universität Innsbruck

    Quantum processors have taken the binary paradigm of classical computing to the quantum realm and are starting to outperform the best classical devices. Yet, neither the underlying quantum information carriers, nor many of the targeted problems naturally fit into this two-level paradigm. In this project, I aim to break this paradigm. Instead of restricting the rich Hilbert space of trapped ions to only two levels, the proposed research will make full use of the multi-level (qudit) structure as a resource for quantum information processing. This will unlock unused potential within quantum processors and bring near-term intermediate-scale quantum devices into a regime well beyond classical capabilities. Furthermore, the availability of high-performing qudit quantum hardware will stimulate a rethinking of the way we approach quantum information processing. This ambitious goal will be achieved by designing and implementing a trapped-ion quantum processor, tailored for qudits. Building on the full toolbox of atomic physics, this device will benefit from ongoing developments for binary systems, while featuring significantly extended capabilities, including novel ways of interacting qudits for resource-efficient processing. Using this hardware, we aim to achieve two objectives: First, we will develop tools for and demonstrate native qudit quantum information processing from simulation to computation. Second, we will show that the platform outperforms not only qubit systems but also the best classical devices through the demonstration of a quantum advantage. I am convinced that this project will stimulate a number of research directions beyond its immediate goals, from application-tailored quantum computing, to advanced quantum communication and quantum metrology. My strong background in several quantum technology platforms, as well as my track record in (multi-level) quantum information processing puts me in a unique position to realize the ambitious goals of this project.

  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 789017
    Overall Budget: 2,356,120 EURFunder Contribution: 2,356,120 EUR
    Partners: Universität Innsbruck

    In a quantum engineering approach we aim to create strongly correlated molecular quantum gases for polar molecules confined in an optical lattice to two-dimensional geometry with full quantum control of all de-grees of freedom with single molecule control and detection. The goal is to synthesize a high-fidelity molec-ular quantum simulator with thousands of particles and to carry out experiments on phases and dynamics of strongly-correlated quantum matter in view of strong long-range dipolar interactions. Our choice of mole-cule is the KCs dimer, which can either be a boson or a fermion, allowing us to prepare and probe bosonic as well as fermionic dipolar quantum matter in two dimensions. Techniques such as quantum-gas microscopy, perfectly suited for two-dimensional systems, will be applied to the molecular samples for local control and local readout. The low-entropy molecular samples are created out of quantum degenerate atomic samples by well-established coherent atom paring and coherent optical ground-state transfer techniques. Crucial to this pro-posal is the full control over the molecular sample. To achieve near-unity lattice filling fraction for the mo-lecular samples, we create two-dimensional samples of K-Cs atom pairs as precursors to molecule formation by merging parallel planar systems of K and Cs, which are either in a band-insulating state (for the fermions) or in Mott-insulating state (for the bosons), along the out-of-plane direction. The polar molecular samples are used to perform quantum simulations on ground-state properties and dy-namical properties of quantum many-body spin systems. We aim to create novel forms of superfluidity, to investigate into novel quantum many-body phases in the lattice that arise from the long-range molecular dipole-dipole interaction, and to probe quantum magnetism and its dynamics such as spin transport with single-spin control and readout. In addition, disorder can be engineered to mimic real physical situations.

  • Funder: EC Project Code: 237241
    Partners: Universität Innsbruck
  • Open Access mandate for Publications and Research data
    Funder: EC Project Code: 894799
    Overall Budget: 159,653 EURFunder Contribution: 159,653 EUR
    Partners: Universität Innsbruck

    Quantifying cyber risk is an important step in assigning resources to prevention. Yet data limitations mean that current estimates ignore certain incidents (e.g ransomware), rarely provide the financial cost, and cannot describe how risk varies based on the firm’s revenue or industry. Surprisingly insurers sell cyber insurance for the ignored incident types and vary the price based on firm-specific characteristics. Extracting insurers’ cyber loss models could help firms manage risk, regardless of whether they purchase insurance. The proposed action (QCYRISK) uses an iterative model fitting approach to infer loss distributions from insurance prices. The first research question develops the conceptual foundations by building an economic argument about how much information can be extracted from insurance markets. QCYRISK's second question seeks to infer full cyber loss distributions, including how they vary based on firm-specific characteristics. The final research question adopts an adversarial machine learning approach to probe the validity of the inferences, using both synthetic distributions and real cyber crime data. In terms of results and dissemination, QCYRISK will provide a set of loss distributions for multiple cyber incident types adjusted based on the firm’s revenue and industry. These will be made available as a spreadsheet for real-world risk managers. We will also run a continuing education seminar for insurance professionals to raise awareness about the method. The developed method represents a new computational insurance technique that could be applied to extract information from a global total of €4.7 trillion insurance premiums.

  • Funder: EC Project Code: 253751
    Partners: Universität Innsbruck