Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning.Recent advances in large language models have further improved the ability to extract meaningful representations from clinical texts.However, clinical notes are often missing.For example, in our analysis of the MIMIC-IV dataset, 24.5% of patients have no available discharge summaries.In such cases, representations can be learned from other modalities such as structured data, chest X-rays, or radiology reports.Yet the availability of these modalities is influenced by clinical decision-making and varies across patients, resulting in modality missingnot-at-random (MMNAR) patterns.We propose a causal representation learning framework that leverages observed data and informative missingness in multimodal clinical records.It consists of: (1) an MMNAR-aware modality fusion component that integrates structured data, imaging, and text while conditioning on missingness patterns to capture patient health and clinician-driven assignment; (2) a modality reconstruction component with contrastive learning to ensure semantic sufficiency in representation learning; and (3) a multitask outcome prediction model with a rectifier that corrects for residual bias from specific modality observation patterns.Comprehensive evaluations across MIMIC-IV and eICU show consistent gains over the strongest baselines, achieving up to 13 .8% improvement for hospital readmission and 13 .1 % for ICU admission (AUC, relative to best baseline).

The rapid execution of complex quantum circuit simulations is essential for validating theoretical algorithms, thereby facilitating their successful implementation on quantum hardware. Although mainstream CPU-based platforms for circuit simulations are well established, they tend to be slower. Conversely, the adoption of GPU platforms remains limited because of the necessity for specialized quantum simulation frameworks tailored to different hardware architectures, each requiring distinct implementation and optimization strategies. Therefore, we introduced Q-Gear, a platform-agnostic framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU- and GPU-based simulations by two orders of magnitude and ten times, respectively, with minimal coding effort. Furthermore, Q-Gear leverages the Cuda-Q configuration to interconnect the memory of GPUs, allowing the execution of much larger circuits beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and Shifter image at Perlmutter (NERSC/LBNL), both derived from an NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler, allowing approximately 100% utilization of up to 1,024 GPUs. We present various benchmarks for Q-Gear to demonstrate the efficiency of our computational paradigm.

Abstract Quantum machine learning as a field has emerged rather quickly thanks to developments in quantum computing. Of these developments, the one of primary interest is the Quantum Generative Adversarial Network or QGAN which is an enhancement of the familiar GAN to use quantum computation necessary for producing synthetic images. To sum up, based on different types of experiments, QGANs outperformed classical GANs, especially in cases with images such as MNIST and Fashion MNIST datasets. Nevertheless, their capabilities are not fully comprehensible due to existing constraints in quantum systems technology, especially in the NISQ era. In this regard, the current study undertakes a proposed research direction that focuses on improving the resolution of grayscale images that have been produced from the “optdigits” dataset, which contains handwritten digit images. Our work then contrasts this with prior work in terms of FID scores, loss function values, runtime, and the resolution of the images. Further, we extend the work by carrying out the proposed methodology on the FMNIST dataset and provide results to corroborate the efficacy of the proposed technique, besides enabling comparison on the same platform with prior works.

The study of microstructure of materials is of great significance in the field of materials science. The interdisciplinary cooperation of materials science and computer science makes it more accurate and efficient to explore the relationship between material microstructure and material properties. Machine learning has potential in exploring the relationship between microstructure and properties of materials. This paper proposes adding the morphology features of grains into the construction of grain knowledge graph to enrich the grain information in the graph. First, an autoencoder extracts the grain morphology features and adds them to the grain knowledge graph. Then, the graph convolutional network is used to extract the features of the graph, and the fully connected network is used to predict the properties of the material. Experiments are performed on actual EBSD scanning data. The experimental results show that the proposed method has noticeable improvement over the competing methods.

Quantum cryptography provides absolute security against an all-powerful eavesdropper (Eve). However, in practice Eve's resources may be restricted to a limited aperture size so that she cannot collect all paraxial light without alerting the communicating parties (Alice and Bob). In this paper we study a quantum wiretap channel in which the connection from Alice to Eve is lossy, so that some of the transmitted quantum information is inaccessible to both Bob and Eve. For a pureloss channel under such restricted eavesdropping, we show that the key rates achievable with a two-mode squeezed vacuum state, heterodyne detection, and public classical communication assistance-given by the Hashing inequality-can exceed the secret key distillation capacity of the channel against an omnipotent eavesdropper. We report upper bounds on the key rates under the restricted eavesdropping model based on the relative entropy of entanglement, which closely match the achievable rates. For the pure-loss channel under restricted eavesdropping, we compare the secret-key rates of continuous-variable (CV) quantum key distribution (QKD) based on Gaussian-modulated coherent states and heterodyne detection with the discrete variable (DV) decoystate BB84 QKD protocol based on polarization qubits encoded in weak coherent laser pulses.