QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors

TL;DR

QubiC is an FPGA-based control and measurement system designed for superconducting quantum processors, enabling system-level optimization, efficient complex algorithm execution, and demonstrating high-fidelity qubit operations.

Contribution

It introduces a modular, open-source FPGA-based control system with hardware, gateware, and software, tailored for scalable superconducting quantum processors.

Findings

Achieved single-qubit fidelity of 0.9980

Achieved two-qubit fidelity of 0.948

Demonstrated efficient randomized benchmarking and control protocols

Abstract

As quantum information processors grow in quantum bit (qubit) count and functionality, the control and measurement system becomes a limiting factor to large scale extensibility. To tackle this challenge and keep pace with rapidly evolving classical control requirements, full control stack access is essential to system level optimization. We design a modular FPGA (field-programmable gate array) based system called QubiC to control and measure a superconducting quantum processing unit. The system includes room temperature electronics hardware, FPGA gateware, and engineering software. A prototype hardware module is assembled from several commercial off-the-shelf evaluation boards and in-house developed circuit boards. Gateware and software are designed to implement basic qubit control and measurement protocols. System functionality and performance are demonstrated by performing qubit chip characterization, gate optimization, and randomized benchmarking sequences on a superconducting quantum processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit process fidelities are measured to be 0.9980$\pm$0.0001 and 0.948$\pm$0.004 by randomized benchmarking. With fast circuit sequence loading capability, the QubiC performs randomized compiling experiments efficiently and improves the feasibility of executing more complex algorithms.