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Y48: Superconducting Qubit Signal Delivery

200E

Sponsoring Units: DQIChair: Florent Lecocq, National Institute of Standards and Technology BoulderSession Tags:
  • Focus

Fri. March 8, 8:00 a.m. – 8:12 a.m. CST

200E

Researchers manipulate and measure quantum processing units via the classical electronics control system. We developed an open-source FPGA-based quantum bit control system called QubiC for superconducting qubits. After a few years of qubit calibration and testing experience on QubiC 1.0, we recognized the need for mid-circuit measurements and feed-forward capabilities to implement advanced quantum algorithms effectively. Moreover, following the development of RFSoC technology, we upgraded the system to QubiC 2.0 on an Xilinx ZCU216 evaluation board and developed all these enriched features. The system uses portable FPGA gateware with a custom processor to execute pulse commands on the fly. For design simplicity and straightforward scaling, we adopted a multi-core distributed architecture, assigning one processor core per qubit. The actual pulses combine the unique pulse envelope and carrier information specified in a command. Each pulse envelope is pre-stored on FPGA's block RAMs, ensuring the speed and reusability during the whole circuit. The pulse parameters including amplitude, phase, and frequency can be updated from pulse to pulse. The software stack is developed in Python, running on both the FPGA's ARM core and host computer via XML-RPC. The quantum circuit can be described in a high-level language, which supports programming at both pulse-level and native-gate level and includes high-level control flow constructs. The QubiC software stack compiles these quantum programs into binary commands that can be loaded into the FPGA. With Qubic 2.0, we successfully achieved multi-FPGA synchronization and demonstrated feed-forward experiments on conditional circuits. The enhanced QubiC system represents a significant step forward in quantum computing, providing researchers with powerful tools to explore and implement advanced quantum algorithms and applications.

Presented By

  • Gang Huang (Lawrence Berkeley National Laboratory)

Authors

  • Gang Huang (Lawrence Berkeley National Laboratory)
  • Yilun Xu (Lawrence Berkeley National Laboratory)
  • Neelay Fruitwala (Lawrence Berkeley National Lab)
  • Abhi D Rajagopala (Lawrence Berkeley National Laboratory)
  • Ravi K Naik (Lawrence Berkeley National Laboratory)
  • Kasra Nowrouzi (Lawrence Berkeley National Laboratory)
  • David I Santiago (Lawrence Berkeley National Laboratory)
  • Irfan Siddiqi (University of California, Berkeley)