Fabrizio Minganti

High-kinetic inductance NbN films for high-quality compact superconducting resonators

  1. Simone Frasca,
  2. Ivo Nikolaev Arabadzhiev,
  3. Sebastien Yves Bros de Puechredon,
  4. Fabian Oppliger,
  5. Vincent Jouanny,
  6. Roberto Musio,
  7. Marco Scigliuzzo,
  8. Fabrizio Minganti,
  9. Pasquale Scarlino,
  10. and Edoardo Charbon
Niobium nitride (NbN) is a particularly promising material for quantum technology applications, as entails the degree of reproducibility necessary for large-scale of superconducting
circuits. We demonstrate that resonators based on NbN thin films present a one-photon internal quality factor above 105 maintaining a high impedance (larger than 2kΩ), with a footprint of approximately 50×100 μm2 and a self-Kerr nonlinearity of few tenths of Hz. These quality factors, mostly limited by losses induced by the coupling to two-level systems, have been maintained for kinetic inductances ranging from tenths to hundreds of pH/square. We also demonstrate minimal variations in the performance of the resonators during multiple cooldowns over more than nine months. Our work proves the versatility of niobium nitride high-kinetic inductance resonators, opening perspectives towards the fabrication of compact, high-impedance and high-quality multimode circuits, with sizable interactions.
arxiv pdf

Dispersive qubit readout with machine learning

  1. Enrico Rinaldi,
  2. Roberto Di Candia,
  3. Simone Felicetti,
  4. and Fabrizio Minganti
Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. For example, it can be used to enhance the fidelity
of superconducting qubit readout measurements, a central problem toward the creation of reliable quantum hardware. A recently introduced measurement protocol, named „critical parametric quantum sensing“, uses the parametric (two-photon driven) Kerr resonator’s driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9\% [arXiv:2107.04503]. In this work, we improve upon the previous protocol by using machine learning-based classification algorithms to \textit{efficiently and rapidly} extract information from this critical dynamics, which has so far been neglected to focus only on stationary properties. These classification algorithms are applied to the time series data of weak quantum measurements (homodyne detection) of a circuit-QED implementation of the Kerr resonator coupled to a superconducting qubit. This demonstrates how machine learning methods enable a faster and more reliable measurement protocol in critical open quantum systems.
arxiv pdf