APS Logo

Time reversal based hybrid quantum state transfer scheme and its application in coupling microwave systems

ORAL

Abstract

We consider the deterministic transfer of a quantum state between two memory nodes of a quantum network using flying qubits, e.g., itinerant photons. We are particularly interested in hybrid cases where the photon produced by one node has to be transformed before it can interface efficiently with a receiving node. Such transformations could help enable the distribution of quantum states (and hence entanglement) between heterogeneous nodes, such as connecting an atom-based quantum memory and a solid-state computation unit. We show how and why the probability of interfacing successfully is determined by the overlap of the spectral shape of the actual flying qubit and the ideal shape. This allows us to analytically and numerically analyze how the probability of success is impacted by realistic errors and show the utility of our scheme (in consonance with known error correction methods).



Our initial work focused on a concrete implementation in which the memory nodes consist of three-level atoms in cavities (with potentially different physical parameters) and the flying qubits are optical photons, though the scheme generalizes to other systems provided the transformation can be implemented. For such optical photons, the desired transformation – including time reversal – has a proposed nonlinear frequency conversion-based implementation, which is ‘blind’ in that it can perform the desired transformation on an input state with arbitrary temporal shape. Our current work is in developing analogous transformations in the microwave domain. Due to the five orders of magnitude scale difference between optical and microwave light, alternative nonlinear processes must be used.

Publication: "Success probabilities in time reversal based hybrid quantum state transfer," preprint arXiv:2401.08110 (2024).<br>"Quantum state transfer and input-output theory with time reversal," Phys. Rev. A ,108 012421 (2023).<br>

Presenters

  • Kevin J Randles

    University of Oregon

Authors

  • Kevin J Randles

    University of Oregon

  • Steven J Van Enk

    University of Oregon