Alessandro Reineri

Microwave dielectric properties of LiNbO3 and AlN at millikelvin temperatures and single-photon power

  1. Alessandro Reineri,
  2. Francesco Crisa,
  3. Akshay Murthy,
  4. Maithlee Shinde,
  5. Daniel Bafia,
  6. Changqing Wang,
  7. Tanay Roy,
  8. Alexander Romanenko,
  9. John Zasadzinski,
  10. Anna Grassellino,
  11. and Silvia Zorzetti
Efficient bidirectional microwave optical photon conversion is a key capability for scaling superconducting quantum processors into distributed networks. However, achieving the necessary
conversion efficiency requires filling a critical knowledge gap in understanding the loss mechanisms of electro optic materials. Here, we characterize the microwave properties of single crystal bulk LiNbO3 and AlN over a broad range of powers, down to single photon levels, and spanning from millikelvin temperatures to above 1K. We demonstrate that both materials exhibit two level systems (TLS) behavior, while piezoelectric related losses are excluded. We show that TLS induced dissipation is predominantly localized on the surface rather than being an intrinsic bulk property, a result further corroborated by room temperature 3D XPS and time of flight SIMS analyses. These findings provide useful insights to engineer hybrid architectures that integrate bulk electro optic crystals within superconducting cavities, proving that microwave quality factors compatible with high efficiency microwave optical transduction are within reach.
arxiv pdf

Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation

  1. Joseph Yaker,
  2. Jovan Markovic,
  3. Alessandro Reineri,
  4. Doga Murat Kurkcuoglu,
  5. and Silvia Zorzetti
Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits,
become promising architectures for bosonic quantum information processing. The inverse design of such systems, i.e., recovering device geometries that produce specified electromagnetic and coupling targets, is generally a one-to-many problem. The qubit-cavity coupling strength depends sensitively on both the transmon geometry and its position within the cavity’s electromagnetic field. As these systems scale up and their design parameter spaces grow, the cost of conventional iterative simulation becomes prohibitive. We present two deep neural network (DNN) approaches that address this inverse-design problem at complementary levels of the design stack. The first proposes SRF cavity geometries that produce target cavity observables. The second proposes transmon qubit designs that produce target qubit-cavity parameters — the coupling rate, qubit frequency, and anharmonicity (g,νq,α). The recovered candidate designs match the targets to within ∼5\% (cavity) and ∼2\% (transmon), confirmed by end-to-end re-simulation. Both approaches map desired device behavior directly to candidate designs, a fast alternative to the iterative simulation studies usually required.
arxiv pdf