Minisymposium Presentation

Quantum computing has rapidly evolved in recent years, with platforms ranging from superconducting qubits and trapped ions to photonic and neutral atom systems. These diverse approaches offer unique strengths and challenges, driving the need for reliable methods to assess and optimize device performance. However, characterizing and benchmarking these quantum processors is challenging, particularly because the quantum state space grows exponentially with system size, making standard methods impractical. In this talk, I will introduce randomized measurements, a feasible solution that leverages random measurement bases to extract essential classical information from complex quantum states. This efficient conversion of quantum data enables deeper insights into quantum dynamics and to improve device calibration. Moreover, it integrates seamlessly with classical HPC and machine learning workflows, harnessing the resulting classical data. In this context, I will present RandomMeas.jl, an open-source Julia package that simplifies and standardizes the application of these measurement techniques. By equipping researchers with accessible tools for advanced quantum state characterization, this aims to foster broader adoption and innovation across the quantum computing community.