Robust manipulation of superconducting qubits in the presence of fluctuations

TL;DR

This paper introduces a sampling-based learning control method to design robust control fields for superconducting qubits, effectively handling fluctuations and inaccuracies in quantum systems.

Contribution

The paper presents a novel sampling-based learning control approach for robust manipulation of superconducting qubits, addressing practical fluctuations and defects.

Findings

Achieves robust control of one-qubit systems.

Successfully manipulates coupled two-qubit systems.

Demonstrates resilience against large fluctuations.

Abstract

Superconducting quantum systems are promising candidates for quantum information processing due to their scalability and design flexibility. However, the existence of defects, fluctuations, and inaccuracies is unavoidable for practical superconducting quantum circuits. In this paper, a sampling-based learning control (SLC) method is used to guide the design of control fields for manipulating superconducting quantum systems. Numerical results for one-qubit systems and coupled two-qubit systems show that the "smart" fields learned using the SLC method can achieve robust manipulation of superconducting qubits, even in the presence of large fluctuations and inaccuracies.