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Pan, Wei

Publications and source records attributed to Pan, Wei.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Systems and methods for resolving a number of incident RF-range photons

A photon-number-resolving detector comprises a detection element, an ohmmeter, and a hardware logic component. The detection element can be formed from a Weyl or Dirac semimetal. Electrons of the detection element are characterized by a surface state that exhibits a Dirac cone and a bulk superconducting state that exhibits a bandgap. When photons having energies less than the bandgap of the bulk superconducting state impinges on the detection element, the photons can be absorbed by electrons of the detection element that are characterized by the surface state. The ohmmeter outputs resistance data indicative of an electrical resistance of the detection element while the photons impinge on the detection element. The hardware logic component can determine, based upon the resistance data, a number of the photons that are absorbed by the surface state electrons of the detection element.

Soh, Daniel Beom Soo↗
DOE OSTI2024

Edge majorana quasiparticles and qubits

Various embodiments described herein provide for a topological quantum computer that uses edge Majorana quasi-particles to form qubits. An inverted Indium Arsenide (InAs) and Gallium Antimonide (GaSb) heterostructure is disclosed that is a quantum spin Hall insulator. A layer of aluminum can be deposited over a nanotube that is placed across the layers of the heterostructure. Once the nanotube is removed, and a gate is formed on the heterostructure and the heterostructure is cooled so that the aluminum becomes superconducting, helical edge states are formed at the junction of the super conducting aluminum, the InAs, and the GaSb which creates a Majorana zero modes (MZMs) at zero magnetic field. The MZMs can be used to construct a topological qubit for fault-resistant topological quantum computation.

Pan, Wei↗
DOE OSTI2023

Deep Koopman learning of nonlinear time-varying systems

Here this paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which results from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.

97 MATHEMATICS AND COMPUTING↗