Emanuele Palumbo

Simulation Framework for the Automated Search of Optimal Parameters Using Physically Relevant Metrics in Nonlinear Superconducting Quantum Circuits

  1. Emanuele Palumbo,
  2. Alessandro Alocco,
  3. Andrea Celotto,
  4. Luca Fasolo,
  5. Bernardo Galvano,
  6. Patrizia Livreri,
  7. and Emanuele Enrico
In this contribution we present this http URL (JCO), a simulation and optimization framework based on the this http URL library for Julia. It models superconducting circuits that include
Josephson junctions (JJs) and other nonlinear elements within a lumped-element approach, leveraging harmonic balance, a frequency-domain technique that provides a computationally efficient alternative to traditional time-domain simulations. JCO automates the evaluation of optimal circuit parameters by implementing Bayesian optimization with Gaussian processes through a device-specific metric and identifying the optimal working point to achieve a defined performance function. This makes it well suited for circuits with strong nonlinearity and a high-dimensional set of coupled design parameters. To demonstrate its capabilities, we focus on optimizing a Josephson Traveling-Wave Parametric Amplifier (JTWPA) based on Superconducting Nonlinear Asymmetric Inductive eLements (SNAILs), operating in the three-wave mixing regime. The device consists of an array of unit cells, each containing a loop with multiple JJs, that amplifies weak quantum signals near the quantum noise limit. By integrating efficient simulation and optimization strategies, the framework supports the systematic development of superconducting circuits for a broad range of applications.
arxiv pdf

Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting

  1. Alessandro D'Elia,
  2. Boulos Alfakes,
  3. Anas Alkhazaleh,
  4. Leonardo Banchi,
  5. Matteo Beretta,
  6. Stefano Carrazza,
  7. Fabio Chiarello,
  8. Daniele Di Gioacchino,
  9. Andrea Giachero,
  10. Felix Henrich,
  11. Alex Stephane Piedjou Komnang,
  12. Carlo Ligi,
  13. Giovanni Maccarrone,
  14. Massimo Macucci,
  15. Emanuele Palumbo,
  16. Andrea Pasquale,
  17. Luca Piersanti,
  18. Florent Ravaux,
  19. Alessio Rettaroli,
  20. Matteo Robbiati,
  21. Simone Tocci,
  22. and Claudio Gatti
In this paper we report the use of superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and
characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, like the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on the single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favouring applications like axion dark matter searches.
arxiv pdf