Precision and resource scaling of real-time flux distortion compensation for superconducting quantum control

  1. Qi Zhou,
  2. Zi-Hao Mei,
  3. Peng Duan,
  4. Peng Wang,
  5. Liang-Liang Guo,
  6. Hao-Ran Tao,
  7. Wei-Cheng Kong,
  8. Hui Yang,
  9. Guo-Ping Guo,
  10. and Zhao-Yun Chen
Real-time waveform generation supports dynamic quantum circuits without pre-storing complete waveforms for every execution path. However, long-lived distortions in flux-control lines
degrade gate fidelity, requiring compensation to account for the actual pulse history. A frequency-domain inversion and time-domain fitting method is proposed for resource-efficient real-time flux distortion compensation. The method fits the reconstructed compensation impulse response with a compact hybrid infinite impulse response (IIR) and finite impulse response (FIR) filter. Look-ahead parallelization enables this filter to process synthesized waveforms at 1.2GSa/s on a field-programmable gate array (FPGA). Two-qubit cross-entropy benchmarking shows that real-time IIR filtering achieves a median controlled-Z Pauli fidelity close to the software-reference value of 99.57%. Numerical analysis and FPGA synthesis indicate approximately logarithmic growth in hardware resource use with compensation timescale. Extending compensation from microsecond to hundred-microsecond timescales increases look-up table (LUT) and digital signal processing (DSP) resource use by only about 14% and 4%, respectively, while maintaining a relative arithmetic error below 10−4. This work provides a scalable hardware foundation for high-fidelity flux control in dynamic superconducting quantum circuits.

Anti-crosstalk high-fidelity state discrimination for superconducting qubits

  1. Zi-Feng Chen,
  2. Qi Zhou,
  3. Peng Duan,
  4. Wei-Cheng Kong,
  5. Hai-Feng Zhang,
  6. and Guo-Ping Guo
Measurement for qubits plays a key role in quantum computation. Current methods for classifying states of single qubit in a superconducting multi-qubit system produce fidelities lower
than expected due to the existence of crosstalk, especially in case of frequency crowding. Here, We make the digital signal processing (DSP) system used in measurement into a shallow neural network and train it to be an optimal classifier to reduce the impact of crosstalk. The experiment result shows the crosstalk-induced readout error deceased by 100% after a 3-second optimization applied on the 6-qubit superconducting quantum chip.