Quantum computing for compressible flow simulations: this proposal is aimed at exploiting the promise of quantum computing to solve compressible flow problems. Two different approaches, namely…

The University of Pittsburgh has been awarded a $249,901 Cooperative Agreement by a civilian federal agency's Shared Services Center to conduct research on quantum computing applications for compressible flow simulations. The contract, effective January 1, 2025 through December 31, 2026, will explore two distinct methodologies: quantum-ready simulations and quantum-inspired simulations, to evaluate their respective advantages and disadvantages relative to classical computational techniques. Given the nascent state of quantum computing technology, the initial research phase will focus on simple canonical problems, with potential expansion to more practical applications in subsequent efforts should the research demonstrate clear advantages over conventional approaches.

The work will be performed in Pittsburgh, Pennsylvania, leveraging the University of Pittsburgh's extensive research infrastructure and expertise. This award reflects the university's established capability as a premier research institution within the federal contracting landscape, having received substantial federal funding across the National Institutes of Health, Department of Defense, and other agencies for advanced scientific research. No set-aside designation was utilized in this procurement, indicating open competitive bidding procedures.

Quantum computing for compressible flow simulations: this proposal is aimed at exploiting the promise of quantum computing to solve compressible flow problems. Two different approaches, namely quantum-ready simulations and quantum-inspired simulations , will be explored to understand the advantages and disadvantages with respect to the solution of compressible flows. Since quantum computing is in its infancy, initial application will be to simple canonical problems during the proposed research. If clear advantages over classical computational techniques are demonstrated, the new approach can be applied to more practical problems in subsequent research efforts.