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Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00381690" target="_blank" >RIV/68407700:21230/23:00381690 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1109/TQE.2023.3314839" target="_blank" >https://doi.org/10.1109/TQE.2023.3314839</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/TQE.2023.3314839" target="_blank" >10.1109/TQE.2023.3314839</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing

  • Original language description

    Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing the number of characteristics, such as the cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted to cover exposure arising from a given transaction or a set of transactions. One of the common objectives is to minimize the cost of collateral required to mitigate the risk associated with a particular transaction or a portfolio of transactions while ensuring sufficient protection for the involved parties. Often, this results in a large-scale combinatorial optimization problem. In this study, we initially present a mixed-integer linear programming formulation for the collateral optimization problem, followed by a quadratic unconstrained binary optimization (QUBO) formulation in order to pave the way toward approaching the problem in a hybrid-quantum and noisy intermediate-scale quantum-ready way. We conduct local computational small-scale tests using various software development kits and discuss the behavior of our formulations as well as the potential for performance enhancements. We find that while the QUBO-based approaches fail to find the global optima in the small-scale experiments, they are reasonably close suggesting their potential for large instances. We further survey the recent literature that proposes alternative ways to attack combinatorial optimization problems suitable for collateral optimization.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    IEEE Transactions on Quantum Engineering

  • ISSN

    2689-1808

  • e-ISSN

    2689-1808

  • Volume of the periodical

    4

  • Issue of the periodical within the volume

    October

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    18

  • Pages from-to

  • UT code for WoS article

    001363364800003

  • EID of the result in the Scopus database

    2-s2.0-85171599377