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Creators/Authors contains: "Englund, D."

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  1. Abstract We propose an integrated photonics device for mapping qubits encoded in the polarization of a photon onto the spin state of a solid-state defect coupled to a photonic crystal cavity: a “polarization-encoded photon-to-spin interface” (PEPSI). We perform a theoretical analysis of the state fidelity’s dependence on the device’s polarization extinction ratio and atom–cavity cooperativity. Furthermore, we explore the rate-fidelity trade-off through analytical and numerical models. In simulation, we show that our design enables efficient, high fidelity photon-to-spin mapping.
  2. Optical approaches to AI acceleration have gained intense interest recently due to the potentially breakthrough advantages of photonics: high bandwidth, low power consumption, and efficient data movement. We overview leading photonic AI platforms based on beamsplitter mesh networks, weight banks, and photoelectric multiplication. While the theoretical performance can be orders of magnitude beyond current state of the art, practical issues of chip area, input / output, and crosstalk paint a more nuanced near-term picture of photonic AI acceleration. Both fundamental and near-term limitations to energy efficiency are addressed, and bandwidth limitations due to temporal crosstalk are analyzed.