5 papers
Quantum resources in non-stoquastic quantum annealing
Chiara Capecci, Sebastian Nagies, Naga Dileep Varikuti +1
Quantum annealing promises to solve combinatorial optimization problems by preparing the ground state of a target Hamiltonian. Standard annealing protocols are, however, stoquastic…
Practical Noise Mitigation for Quantum Annealing via Dynamical Decoupling: Toward Industry-Relevant Optimization using Trapped Ions
Sebastian Nagies, Chiara Capecci, Marcel Seelbach Benkner +6
Quantum annealing is a framework for solving combinatorial optimization problems. While it offers a promising path towards a practical application of quantum hardware, its performa…
Enhancing Expressivity of Quantum Neural Networks Based on the SWAP test
Sebastian Nagies, Emiliano Tolotti, Davide Pastorello +1
Quantum neural networks (QNNs) based on parametrized quantum circuits are promising candidates for machine learning applications, yet many architectures lack clear connections to c…
Boosting quantum annealing performance through direct polynomial unconstrained binary optimization
Sebastian Nagies, Kevin T. Geier, Javed Akram +3
Quantum annealing aims at solving optimization problems of practical relevance using quantum-computing hardware. Problems of interest are typically formulated in terms of quadratic…
The role of higher-order terms in trapped-ion quantum computing with magnetic gradient induced coupling
Sebastian Nagies, Kevin T. Geier, Javed Akram +5
Trapped-ion hardware based on the Magnetic Gradient Induced Coupling (MAGIC) scheme is emerging as a promising platform for quantum computing. Nevertheless, in this -- as in any ot…