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A Customized FPGA-Based Control System for Superconducting Qubits

ORAL

Abstract

A quantum control system is crucial for precise and reliable quantum computations. Existing off-the-shelf hardware lacks functional flexibility, decision-making speed and is not scalable. We address these limitations by developing custom FPGA control hardware. The system includes the frontend (QUASAR) that acts as an interface between the user software and low-level control electronics, and the backend (QubiC) that hides the complexity of the microwave generation and analog-to-digital conversion of the readout. The key component of the frontend is the control processor that manages quantum program execution. We developed QUASAR to facilitate quantum program execution for high control operation speed and future scalability. QubiC is an LBNL in-house developed open-source backend that provides a full control stack to the low-level hardware. As a result of the close proximity of the control processor and the analog backend, the extended quantum architecture allows us to make control decisions efficiently based on the measurement necessary for fast feedback. We demonstrated our custom control system on a Xilinx FPGA that can be used for a variety of quantum experiments with a superconducting quantum information processor.

Publication: A. Butko et al., "Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization," 2020 International Conference on Rebooting Computing (ICRC), 2020, pp. 66-75, doi: 10.1109/ICRC2020.2020.00011.<br>Xu, Y et al., "QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors", 2020, arXiv e-prints arXiv:2101.00071.

Presenters

  • Anastasiia Butko

    Lawrence Berkeley National Laboratory

Authors

  • Anastasiia Butko

    Lawrence Berkeley National Laboratory

  • Yilun Xu

    Lawrence Berkeley National Laboratory

  • Gang Huang

    Lawrence Berkeley National Laboratory

  • Thorsten Stezelberger

    Lawrence Berkeley National Laboratory

  • David I Santiago

    Lawrence Berkeley National Laboratory, Computational Research Division, Lawrence Berkeley National Lab

  • Irfan Siddiqi

    University of California, Berkeley, Applied Mathematics and Computational Research and Materials Sciences Divisions, LBNL, Lawrence Berkeley National Laboratory, Applied Mathematics, Computational Research and Materials Sciences Divisions, Lawrence Berkeley National Lab