Parametric multi-qubit architectures for superconducting quantum circuits
ORAL
Abstract
Parametric interactions provide a fast and selective way to couple qubits and superconducting resonators. These interactions, activated by modulating a tunable element at a linear combination of the qubit and resonator (or other qubit) frequencies, facilitate strong coupling between resonant modes that are far detuned in frequency, thus delivering a flexible and robust approach. Furthermore, the tunable coupler needs to provide high coupling rate and on-off ratio, to suppress unwanted parasitic interactions when idle. A modular design, suitable to couple qubits to each other and to resonators and waveguides, is desirable to enable both unitary [1] and dissipative [2] operations as well as long-range interconnects.
In this talk we describe dual and multi-qubit parametric circuit architectures. By connecting multiple qubits to a common resonator and injecting suitable parametric pumps, we can engineer fast dissipative stabilization of entanglement without speed-fidelity trade off [2]. While a single coupler provides a flexible platform to couple multiple elements with reduced wiring count, dual-transmon couplers provide intrinsic ZZ cancellation and greater modularity. We will discuss both approaches and present our latest experimental data.
In this talk we describe dual and multi-qubit parametric circuit architectures. By connecting multiple qubits to a common resonator and injecting suitable parametric pumps, we can engineer fast dissipative stabilization of entanglement without speed-fidelity trade off [2]. While a single coupler provides a flexible platform to couple multiple elements with reduced wiring count, dual-transmon couplers provide intrinsic ZZ cancellation and greater modularity. We will discuss both approaches and present our latest experimental data.
*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of under Award Number DE-SC0019461.
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Publication: [1] Physical Review Applied 19.6 (2023): 064043.
[2] Nature communications 13.1 (2022): 3994.
Presenters
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Leonardo M Ranzani
- RTX BBN Technologies