SFQ-based asynchronous persistent-current biasing grid for tunable-coupler QPU

ORAL

Abstract

Current development efforts of superconducting quantum processing units (QPUs) focus on increasing the qubit count and quantum operation fidelity in tandem. Larger qubit numbers translate to proportionally higher cabling counts and cooling power requirements. Superconducting digital electronics such as Single Flux Quantum (SFQ) or Adiabatic Quantum Flux Parametron (AQFP) families [1] offer a way to overcome some of these system engineering challenges. SFQ technology is particularly attractive for building a programmable local flux source that is dissipationless once programmed. The solution is well-suited for use with tunable-coupler QPU architectures, for instance.



In this work, we present a single-flux quantum circuit that allows row and column addressing of persistent current cells. Flux quanta in each cell can be added or subtracted at integer precision [2]. The total number of control wires can be reduced from N to , where N is the number of flux-tunable elements on the QPU. Below 4K, we have measured flux tunability over a full range of in a galvanically separated SQUID loop with 7 bits of resolution. Independent addressing is also demonstrated. Finally, we will discuss the interfacing of this type of persistent current modules with a QPU.

[1] R. N. Das et al., "Large Scale Cryogenic Integration Approach for Superconducting High-Performance Computing," 2017 IEEE 67th Electronic Components and Technology Conference (ECTC), Orlando, FL, USA, 2017, pp. 675-683, doi: 10.1109/ECTC.2017.54.



[2] Tsuga Yuto, Yuki Yamanashi, and Nobuyuki Yoshikawa. "Asynchronous digital SQUID magnetometer with an on-chip magnetic feedback for improvement of magnetic resolution." IEEE transactions on applied superconductivity 23.3 (2012): 1601405-1601405.

Publication: U. Yilmaz, P. Lähteenmäki, A. Sharafiev and O. P. Saira, "Persistent-current biasing of a tunable-coupler QPU", Poster presented at: 2023 Superconducting Qubits and Algorithms Conference, 29 August - 01 September 2023, Munich, Germany

Presenters

  • Ugur Yilmaz

    IQM Germany GmbH

Authors

  • Ugur Yilmaz

    IQM Germany GmbH

  • Pasi Lähteenmäki

    IQM Finland OY

  • Aleksei Sharafiev

    IQM Germany GmbH

  • Olli-Pentti Saira

    IQM Finland OY