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Auflistung nach Autor:in "Wappler, Markus"

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  • Konferenzbeitrag
    Connecting the Hamiltonian structure to the QAOA performance and energy landscape
    (INFORMATIK 2024, 2024) Müssig, Daniel; Wappler, Markus; Lenk, Steve; Lässig, Jörg
    Quantum computing holds promise for outperforming classical computing in specialized applications such as optimization. With current Noisy Intermediate Scale Quantum (NISQ) devices, only variational quantum algorithms like the Quantum Alternating Operator Ansatz (QAOA) can be practically run. QAOA is effective for solving Quadratic Unconstrained Binary Optimization (QUBO) problems by approximating Quantum Annealing via Trotterization. Successful implementation on NISQ devices requires shallow circuits, influenced by the number of variables and the sparsity of the augmented interaction matrix. This paper investigates the necessary sparsity levels for augmented interaction matrices to ensure solvability with QAOA. By analyzing the Max-Cut problem with varying sparsity, we provide insights into how the Hamiltonian density affects the QAOA performance. Our findings highlight that, while denser matrices complicate the energy landscape, the performance of QAOA remains largely unaffected by sparsity variations. This study emphasizes the algorithm’s robustness and potential for optimization tasks on near-term quantum devices, suggesting avenues for future research in enhancing QAOA for practical applications.
  • Konferenzbeitrag
    Constrained Grover Adaptive Search for Optimization of the Bidirectional EV Charging Problem
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Federer, Marika; Lenk, Steve; Müssig, Daniel; Wappler, Markus; Lässig, Jörg
    The optimization problem of bidirectional electrical vehicle charging (Vehicle-to-Home) becomes more and more important with rising energy prices and the required reduction of 𝐶𝑂2 emissions. We maximize the usage of local solar power generation, while minimizing the power grid usage. This is constrained by the energy demand of the household and the required state of charge at departure as well as the idle times of the car at home. The problem is formulated as a Constrained Polynomial Binary Optimization (CPBO) problem, which is convenient for Grover Adaptive Search by representing the objective function and the constraints as a Quantum Dictionary.