Bradley Mitchell

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

  1. Yilun Xu,
  2. Gang Huang,
  3. Jan Balewski,
  4. Ravi Naik,
  5. Alexis Morvan,
  6. Bradley Mitchell,
  7. Kasra Nowrouzi,
  8. David I. Santiago,
  9. and Irfan Siddiqi
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±0.0001 and 0.948±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.
arxiv pdf

Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

  1. Akel Hashim,
  2. Ravi K. Naik,
  3. Alexis Morvan,
  4. Jean-Loup Ville,
  5. Bradley Mitchell,
  6. John Mark Kreikebaum,
  7. Marc Davis,
  8. Ethan Smith,
  9. Costin Iancu,
  10. Kevin P. O'Brien,
  11. Ian Hincks,
  12. Joel J. Wallman,
  13. Joseph Emerson,
  14. and Irfan Siddiqi
The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale
quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors, and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to maximally-leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.
arxiv pdf