Accurate and low-latency quantum state discrimination is essential for protocols involving mid-circuit measurement (MCM) and conditional feed-forward. In superconducting quantum systems, conventional readout pipelines transfer measurement data to host processors for post-processing, introducing millisecond-scale delays that far exceed qubit coherence times.

Superconducting qubits have emerged as leading candidates for realizing quantum computers, which are particularly useful for solving computational problems that are beyond the capabilities of classical supercomputers. The performance of such systems critically depends on the precision and reliability of their control infrastructure. In this paper, we present an overview of state-of-the-art quantum control systems, including both closed- and open-source solutions, and discuss their respective advantages and limitations. While we review both categories, we focus more extensively on open-source control platforms and explore how their flexibility and programmability can support the research community and drive progress in the field. Lastly, we outline several promising research directions in quantum computing infrastructure, including scalable control, high-precision readout, and leakage suppression. We believe that these areas should be prioritized by the community, as they are critical to realizing fault-tolerant quantum computation.

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD ’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, head-based signal processing systems often produce complex signals, making wearable inference for diagnosis impractical, especially when the signals are weak. This is common with head-worn sensors due to poor contact. Moreover, the real-time transmission of a large corpus of physiological signals over extended periods consumes significant power and time, limiting the viability of battery-dependent physiological monitoring headwear. To address these issues, this paper presents a deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:585 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the clinic and deploy the solution on a commonly used embedded AI chip for headwear systems (i.e., ARM Cortex). The proposed framework achieves a 91% seizure detection accuracy, confirming the approach's reliability, practicality, and scalability.