F
Fengping Jin
Researcher at Forschungszentrum Jülich
Publications - 84
Citations - 1339
Fengping Jin is an academic researcher from Forschungszentrum Jülich. The author has contributed to research in topics: Quantum & Photon. The author has an hindex of 19, co-authored 72 publications receiving 1010 citations.
Papers
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Event-by-event simulation of nonclassical effects in two-photon interference experiments
TL;DR: In this paper, a corpuscular simulation model for second-order intensity interference phenomena is discussed, and it is shown that both the visibility and the visibility of two-photon interference experiments with two independent sources can be explained in terms of a locally causal, modular, adaptive, corpuscular, classical (non-Hamiltonian) dynamical system.
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Screening and Collective Modes in Disordered Graphene Antidot Lattices
TL;DR: In this article, the collective modes of GALs with respect to single-layer graphene antidot lattices were studied in the context of a tight-binding model and the dynamical polarizability and dielectric function were calculated within the random-phase approximation.
Journal ArticleDOI
Real-time simulation of flux qubits used for quantum annealing
Madita Willsch,Madita Willsch,Dennis Willsch,Dennis Willsch,Fengping Jin,Hans De Raedt,Kristel Michielsen,Kristel Michielsen +7 more
TL;DR: In this article, the real-time flux dynamics of up to three superconducting quantum interference devices (SQUIDs) are studied by numerically solving the time-dependent Schrodinger equation.
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General error mitigation for quantum circuits
Manpreet Singh Jattana,Manpreet Singh Jattana,Fengping Jin,Hans De Raedt,Hans De Raedt,Kristel Michielsen,Kristel Michielsen +6 more
TL;DR: In this paper, a general method to mitigate the effect of errors in quantum circuits is outlined, based on characteristics that an ideal method should possess and to ameliorate an existing method which only mitigates state preparation and measurement errors.
Posted Content
GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
TL;DR: In this article, the authors study large-scale applications using a GPU-accelerated version of the massively parallel Julich universal quantum computer simulator (JUQCS--G) using a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), and find that AQA performs surprisingly well in comparison to the quantum approximate optimization algorithm (QAOA).