Learning-based Calibration of Flux Crosstalk in Transmon Qubit Arrays

Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling with system size, while maintaining a median qubit frequency error below $300$ kHz.

[1]  Justyna P. Zwolak,et al.  Automated extraction of capacitive coupling for quantum dot systems , 2023, Physical Review Applied.

[2]  Y. Lai,et al.  Many-body Hilbert space scarring on a superconducting processor , 2022, Nature Physics.

[3]  C. K. Andersen,et al.  Realizing repeated quantum error correction in a distance-three surface code , 2021, Nature.

[4]  G. Guo,et al.  Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination , 2021, Physical Review Applied.

[5]  Justyna P. Zwolak,et al.  Toward Robust Autotuning of Noisy Quantum Dot Devices , 2021, Physical Review Applied.

[6]  A. Fedorov,et al.  Neural networks for on-the-fly single-shot state classification , 2021, Applied Physics Letters.

[7]  Amir H. Karamlou,et al.  Quantum transport and localization in 1d and 2d tight-binding lattices , 2021, npj Quantum Information.

[8]  M. Biercuk,et al.  Experimental Deep Reinforcement Learning for Error-Robust Gate-Set Design on a Superconducting Quantum Computer , 2021, PRX Quantum.

[9]  H. Krovi,et al.  Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States , 2021, Physical Review Applied.

[10]  Amir H. Karamlou,et al.  Probing quantum information propagation with out-of-time-ordered correlators , 2021, Nature Physics.

[11]  S. Filipp,et al.  Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits , 2020, Physical Review Applied.

[12]  Jonathan L. DuBois,et al.  Improving qubit readout with hidden Markov models , 2020, Physical Review A.

[13]  Dino Sejdinovic,et al.  Quantum device fine-tuning using unsupervised embedding learning , 2020, New Journal of Physics.

[14]  Michael A. Osborne,et al.  Machine learning enables completely automatic tuning of a quantum device faster than human experts , 2020, Nature Communications.

[15]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[16]  K. W. Chan,et al.  Autonomous Tuning and Charge-State Detection of Gate-Defined Quantum Dots , 2019, Physical Review Applied.

[17]  Justyna P. Zwolak,et al.  Autotuning of double dot devices in situ with machine learning. , 2019, Physical review applied.

[18]  Blake R. Johnson,et al.  Methods for Measuring Magnetic Flux Crosstalk between Tunable Transmons , 2019, Physical Review Applied.

[19]  Franco Nori,et al.  Strongly correlated quantum walks with a 12-qubit superconducting processor , 2019, Science.

[20]  Michael A. Osborne,et al.  Efficiently measuring a quantum device using machine learning , 2018, npj Quantum Information.

[21]  Fei Yan,et al.  Tunable Coupling Scheme for Implementing High-Fidelity Two-Qubit Gates , 2018, Physical Review Applied.

[22]  Easwar Magesan,et al.  Machine Learning for Discriminating Quantum Measurement Trajectories and Improving Readout. , 2014, Physical review letters.

[23]  S. Girvin,et al.  Charge-insensitive qubit design derived from the Cooper pair box , 2007, cond-mat/0703002.

[24]  S. Girvin,et al.  ac Stark shift and dephasing of a superconducting qubit strongly coupled to a cavity field. , 2004, Physical review letters.

[25]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..