Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout-a critical factor in achieving high-fidelity operations. While current methods, including deep neural networks, enhance readout accuracy, they typically lack support for mid-circuit measurements essential for quantum error correction, and they usually rely on large, resource-intensive network models. This paper presents KLiNQ, a novel qubit readout architecture leveraging lightweight neural networks optimized via knowledge distillation. Our approach achieves around a $99 \%$ reduction in model size compared to the baseline while maintaining a qubitstate discrimination accuracy of $91 \%$. KLiNQ facilitates rapid, independent qubit-state readouts that enable mid-circuit measurements by assigning a dedicated, compact neural network for each qubit. Implemented on the Xilinx UltraScale+ FPGA, our design can perform the discrimination within 32 ns. The results demonstrate that compressed neural networks can maintain highfidelity independent readout while enabling efficient hardware implementation, advancing practical quantum computing.

With the growing complexity and dynamism of cloud environments, users of operations management solutions are facing a critical headache of "event storms". Understanding and prioritizing reactions to such high volumes of noisy recommendation content for various tasks is beyond the capacities of human operators. This significantly degrades the resolution metrics of performance issues and optimization of infrastructures and applications. We have devised a novel streaming clustering algorithm for processing alerts and discovering Alert Episodes with their evolution tracked in time and space. It is based on the principles of the classical density-based clustering DBSCAN. We learn Unknown Problems applying this algorithm to low-level events within the VMware Aria Operations manager. Those episodes might typically be out of alert definitions coverage and explain new types of emerging incidents. Our solutions with different hyperparameters are prototyped and integrated into the production. We share experimental insights from an internal environment with interesting alert episodes learned and unknown problems of alarms/symptoms discovered with a self-explainable story on where the source of the performance issue stays and how it evolved into a larger problem situation affecting several objects and hierarchy layers. The constructs we introduce help reduce user efforts in making sense of events' waves and perform troubleshooting with relevance. Our solution can be refactored into an independent event management service for cloud operations.