Quantum computing systems, built on individual qubits, are advancing rapidly in scale and accuracy. Accurate characterization of these systems is crucial for predicting performance and developing future quantum computation systems. Experiments have been developed to extract parameters of the dispersive readout system in a superconducting qubit device with automation in mind. The resonator plays a key role in the readout system, with noise affecting the signal-to-noise ratio. These experiments are part of the readout optimization procedure, ensuring the accuracy of models used in designing new circuits for future systems.
Introduction to Quantum Computing Systems and Superconducting Qubits
Quantum computing systems are rapidly advancing in scale and accuracy. These systems are built on an underlying physical level of individual qubits, each of which is an analog device with many physical parameters. The ability to extract these parameters reliably and predict the full system performance based on these measurements is crucial. This has been demonstrated in superconducting qubits, where the cross entropy in a random circuit sampling experiment was well predicted by a model based on single and two-qubit component metrics.
The Importance of Accurate Characterization in Quantum Computing
Accurate characterization is not only important for predicting the performance of existing devices but also for the development of future quantum computation systems. These future systems require faster, more accurate, and lower leakage readout than has been reported in a system with sufficient multiplexing. Achieving this speedup while isolating the qubits from decay through the readout system requires improved circuits. Therefore, it is essential that the models used in designing these new circuits are founded in agreement with experiment.
Development of Experiments for Superconducting Qubit Devices
To ensure the accuracy of these models, a set of experiments has been developed to extract the parameters of the dispersive readout system in a superconducting qubit device. These experiments have been designed with automation in mind, requiring as few prerequisite calibrations as possible, running in parallel and in-situ, and requiring only simple model fitting. These experiments are regularly run on chips with tens of qubits via automation software with minimal human interaction.
The Role of Resonator in Quantum Computing Systems
The resonator plays a crucial role in the readout system of a quantum computing system. The resonator frequency depends on the state of the qubit, with different qubit states leading to different outgoing waves, enabling qubit state measurement. For each measurement shot, the outgoing wave is amplified and digitized, and a complex number is computed. The distance in the complex plane between the two associated points of the two qubit states is then calculated.
The Impact of Noise on Quantum Computing Systems
Noise in the system can add random displacements to the computed complex number. These displacements are typically described by a 2-dimensional Gaussian distribution. The 1-dimensional width of this distribution effectively gives the signal to noise ratio (SNR). The quantum efficiency describes the reduction in SNR due to loss and added noise as the signal travels from the readout resonator to the detector.
Conclusion and Future Outlook
The experiments described in this article are part of the readout optimization procedure used in quantum computing systems. The results of these experiments are used to measure the parameters appearing in the quantum computing model and compare the model’s predictions against the actual experiment. This process ensures the accuracy of the models used in designing new circuits for future quantum computation systems.
The article titled “System Characterization of Dispersive Readout in Superconducting Qubits” was published on February 1, 2024, by authors D. Sank, Alex Opremcak, Andreas Bengtsson, Mostafa Khezri, Zijun Chen, Ofer Naaman, and Alexander N. Korotkov. The research was sourced from arXiv, a repository maintained by Cornell University. The article can be accessed through its DOI reference 10.48550/arxiv.2402.00413.
