Abstract An efficient characterization of QND measurements is an important ingredient toward certifying and improving the performance and scalability of quantum processors. In this work, we introduce a parallel tomography of QND measurements that addresses single- and two-qubit readout on a multi-qubit quantum processor. We provide an experimental demonstration of the tomographic protocol on a 7-qubit IBM-Q device, characterizing the quality of conventional qubit readout as well as generalized measurements such as parity or measurement-and-reset schemes. Our protocol reconstructs the Choi matrices of the measurement processes, extracts relevant quantifiers—fidelity, QNDness, destructiveness—and identifies sources of errors that limit the performance of the device for repeated QND measurements. We also show how to quantify measurement crosstalk and use it to certify the quality of simultaneous readout on multiple qubits.
Fast, high-fidelity qubit initialization is a key primitive for scalable quantum information processing, but conventional reset protocols either require long relaxation times or discard information contained in continuous measurement records. We demonstrate an adaptive measurement-based reset protocol for superconducting qubits that uses Bayesian inference to update the qubit-state estimate after each readout and dynamically adjust the feedback threshold. Unlike fixed-threshold or repeat-until-success (RUS) protocols, the method utilizes the full analog measurement history, enabling the reset decision to become progressively more conservative as confidence in ground-state preparation increases. Implemented with real-time FPGA-based feedback on a dispersively readout transmon qubit, the protocol achieves a ground-state initialization fidelity of
99.44±0.04% after a small number of reset rounds. We further compare adaptive reset with RUS strategies in multi-qubit experiments and show that adaptive thresholding provides deterministic reset duration while maintaining a five-qubit simultaneous-reset fidelity of
98.78±0.19%. These results establish Bayesian adaptive thresholding as a practical and scalable route to fast qubit reset in superconducting quantum processors.
Spin qubits in semiconducting quantum dots are currently limited by slow readout processes, which are orders of magnitude slower than gate operations. In contrast, Andreev spin qubits benefit from fast measurement schemes enabled by the large resonator couplings of superconducting qubits but suffer from reduced coherence during qubit operations. Here, we propose fast and high-fidelity measurement protocols based on an electrically tunable coupling between quantum dot and Andreev spin qubits. In realistic devices, this coupling can be made sufficiently strong to enable high-fidelity readout well below microseconds, potentially enabling mid-circuit measurements. Crucially, the electrical tunability of our coupler permits to switch it off during idle periods, minimizing crosstalk and measurement back-action. Our approach is fully compatible with germanium-based devices and paves the way for scalable quantum computing architectures by leveraging the advantages of heterogeneous qubit implementations.