Control and mitigation of microwave crosstalk effect with superconducting qubits

Abstract

Improving gate performance is vital for scalable quantum computing. The universal quantum computing also requires the gate fidelity to reach a high level. For superconducting quantum processor, which operates in the microwave band, the single-qubit gates are usually realized with microwave driving. The crosstalk between microwave pulses is a non-negligible error source. In this article, we propose an error mitigation scheme to address this crosstalk issue for single-qubit gates. There are three steps in our method. First, by controlling the detuning between qubits, the microwave induced classical crosstalk error can be constrained within the computational subspace. Second, by applying the general decomposition procedure, arbitrary single-qubit gate can be decomposed as a sequence of $\sqrt{X}$ and virtual Z gates. Finally, by optimizing the parameters in virtual Z gates, the error constrained in the computational space can be corrected. Using our method, no additional compensation signals are needed, arbitrary single-qubit gate time will not be prolonged, and the circuit depth containing simultaneous single-qubit gates will also not increase. The simulation results show that, in specific regime of qubit-qubit detuning, the infidelities of simultaneous single-qubit gates can be as low as which without microwave crosstalk.

References in corpus (31)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Surface codes: Towards practical large-scale quantum computation
• Logic gates at the surface code threshold: Superconducting qubits poised for fault-tolerant quantum computing
• Error mitigation for short-depth quantum circuits
• Simple pulses for elimination of leakage in weakly nonlinear qubits
• Efficient Z-Gates for Quantum Computing
• AC-Stark Shift and Dephasing of a Superconducting Qubit Strongly Coupled to a Cavity Field
• Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography
• Complete universal quantum gate set approaching fault-tolerant thresholds with superconducting qubits
• Fidelity of quantum operations
• Quantum walks on a programmable two-dimensional 62-qubit superconducting processor
• Characterization of addressability by simultaneous randomized benchmarking
• A universal quantum circuit for two-qubit transformations with three CNOT gates
• Measuring and Suppressing Quantum State Leakage in a Superconducting Qubit
• Realization of high-fidelity CZ and ZZ-free iSWAP gates with a tunable coupler
• Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers
• Suppression of Qubit Crosstalk in a Tunable Coupling Superconducting Circuit
• Detecting crosstalk errors in quantum information processors
• Experimental quantum adversarial learning with programmable superconducting qubits
• Quantum crosstalk analysis for simultaneous gate operations on superconducting qubits
• Calibrated decoders for experimental quantum error correction
• Calibration of the cross-resonance two-qubit gate between directly-coupled transmons
• Methods for Measuring Magnetic Flux Crosstalk Between Tunable Transmons
• Probing context-dependent errors in quantum processors
• Simulating and mitigating crosstalk
• UCSB final report for the CSQ program: Review of decoherence and materials physics for superconducting qubits
• Cancelling microwave crosstalk with fixed-frequency qubits
• Alibaba Cloud Quantum Development Platform: Surface Code Simulations with Crosstalk
• Spectator Errors in Tunable Coupling Architectures
• Spurious microwave crosstalk in floating superconducting circuits

Cited by in corpus (4)

• A Review of Design Concerns in Superconducting Quantum Circuits
• Markovian Noise Modelling and Parameter Extraction Framework for Quantum Devices
• First-Order Crosstalk Mitigation in Parallel Quantum Gates Driven With Multi-Photon Transitions
• Hamiltonian Learning via Inverse Physics-Informed Neural Networks